Tugas UAS Random Forest, Extra Trees, Boosting

library(dplyr) #glimpse

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidymodels) #splitting

## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──

## ✔ broom        1.0.5     ✔ rsample      1.2.0
## ✔ dials        1.2.0     ✔ tibble       3.2.1
## ✔ ggplot2      3.4.3     ✔ tidyr        1.3.0
## ✔ infer        1.0.5     ✔ tune         1.1.2
## ✔ modeldata    1.2.0     ✔ workflows    1.1.3
## ✔ parsnip      1.1.1     ✔ workflowsets 1.0.1
## ✔ purrr        1.0.1     ✔ yardstick    1.2.0
## ✔ recipes      1.0.8

## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()
## ✖ recipes::step()  masks stats::step()
## • Learn how to get started at https://www.tidymodels.org/start/

library(MASS) 

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

library(rcompanion) #cramer

## Warning: package 'rcompanion' was built under R version 4.3.2

## 
## Attaching package: 'rcompanion'

## The following object is masked from 'package:yardstick':
## 
##     accuracy

library(caret) #data imbalanced

## Loading required package: lattice

## 
## Attaching package: 'caret'

## The following objects are masked from 'package:yardstick':
## 
##     precision, recall, sensitivity, specificity

## The following object is masked from 'package:purrr':
## 
##     lift

library(DMwR) #smote

## Loading required package: grid

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

## 
## Attaching package: 'DMwR'

## The following object is masked from 'package:broom':
## 
##     bootstrap

#library("rJava")
#library("extraTrees")
library("writexl")

## Warning: package 'writexl' was built under R version 4.3.2

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.4
## ✔ lubridate 1.9.2     ✔ stringr   1.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ DMwR::bootstrap()   masks broom::bootstrap()
## ✖ readr::col_factor() masks scales::col_factor()
## ✖ purrr::discard()    masks scales::discard()
## ✖ dplyr::filter()     masks stats::filter()
## ✖ stringr::fixed()    masks recipes::fixed()
## ✖ dplyr::lag()        masks stats::lag()
## ✖ caret::lift()       masks purrr::lift()
## ✖ MASS::select()      masks dplyr::select()
## ✖ readr::spec()       masks yardstick::spec()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(themis)
library(embed)

## Warning: package 'embed' was built under R version 4.3.2

library(DataExplorer)
library(ggpubr)
library(DALEXtra)

## Warning: package 'DALEXtra' was built under R version 4.3.2

## Loading required package: DALEX

## Warning: package 'DALEX' was built under R version 4.3.2

## Welcome to DALEX (version: 2.4.3).
## Find examples and detailed introduction at: http://ema.drwhy.ai/
## 
## 
## Attaching package: 'DALEX'
## 
## The following object is masked from 'package:dplyr':
## 
##     explain

library(rcompanion)
library(randomForest)

## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
## 
## Attaching package: 'randomForest'
## 
## The following object is masked from 'package:ggplot2':
## 
##     margin
## 
## The following object is masked from 'package:dplyr':
## 
##     combine

library(xgboost)

## Warning: package 'xgboost' was built under R version 4.3.2

## 
## Attaching package: 'xgboost'
## 
## The following object is masked from 'package:dplyr':
## 
##     slice

setwd("C:/DATA D/S2 Statistika dan Sains Data/Semester 3/Pemodelan Klasifikasi")

DATA ORI

moklas<-read.csv("data moklas.csv",header = T,sep = ';')
moklas

moklas_numeric <- moklas[sapply(moklas, is.numeric)]
moklas_numeric

glimpse(moklas)

## Rows: 359
## Columns: 13
## $ cabang             <int> 9, 14, 8, 11, 9, 1, 9, 10, 14, 5, 5, 11, 1, 10, 7, …
## $ jenis.kelamin      <int> 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, …
## $ usia               <int> 37, 44, 33, 40, 49, 41, 47, 47, 42, 31, 42, 41, 44,…
## $ pendidikan         <int> 3, 3, 4, 3, 2, 2, 3, 4, 4, 2, 3, 2, 4, 3, 4, 4, 3, …
## $ frekuensi.fashion  <int> 3, 3, 4, 4, 2, 2, 6, 4, 3, 3, 5, 0, 4, 3, 3, 1, 5, …
## $ nilai.fashion      <dbl> 0.3568, 0.5388, 0.3012, 0.9808, 0.1730, 0.6352, 2.4…
## $ frekuensi.footwear <int> 2, 1, 4, 3, 2, 0, 2, 1, 3, 1, 2, 5, 3, 2, 2, 1, 3, …
## $ nilai.footwear     <dbl> 3.0260, 1.5922, 0.4540, 0.2978, 0.1484, 0.6734, 0.5…
## $ frekuensi.lainnya  <int> 3, 2, 4, 4, 1, 2, 5, 4, 3, 3, 5, 0, 1, 2, 3, 0, 5, …
## $ nilai.lainnya      <dbl> 2.2050, 0.4408, 0.9494, 0.9728, 0.4654, 0.9780, 1.8…
## $ total.nilai.tunai  <dbl> 8.43, 1.79, 0.00, 7.55, 1.05, 2.68, 21.26, 4.89, 0.…
## $ lama.member        <int> 13, 39, 18, 46, 1, 7, 16, 24, 5, 49, 45, 1, 24, 32,…
## $ promo              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, …

skimr::skim(moklas)

Data summary

Name	moklas
Number of rows	359
Number of columns	13
_______________________
Column type frequency:
numeric	13
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
cabang	0	1	7.62	3.96	1.00	4.00	8.00	11.00	14.00	▇▆▅▇▇
jenis.kelamin	0	1	1.57	0.50	1.00	1.00	2.00	2.00	2.00	▆▁▁▁▇
usia	0	1	40.14	5.07	26.00	37.00	40.00	44.00	56.00	▂▆▇▃▁
pendidikan	0	1	3.06	0.81	1.00	3.00	3.00	4.00	4.00	▁▃▁▇▆
frekuensi.fashion	0	1	3.06	1.52	0.00	2.00	3.00	4.00	8.00	▃▇▃▃▁
nilai.fashion	0	1	0.78	0.52	0.03	0.39	0.67	1.05	2.63	▇▇▃▁▁
frekuensi.footwear	0	1	3.07	1.63	0.00	2.00	3.00	4.00	8.00	▃▇▃▂▁
nilai.footwear	0	1	0.85	0.62	0.03	0.41	0.69	1.11	3.55	▇▅▂▁▁
frekuensi.lainnya	0	1	2.78	1.49	0.00	2.00	3.00	4.00	7.00	▃▃▇▂▁
nilai.lainnya	0	1	0.85	0.59	0.02	0.43	0.74	1.11	3.03	▇▇▂▁▁
total.nilai.tunai	0	1	2.23	3.43	0.00	0.00	0.67	3.04	23.02	▇▂▁▁▁
lama.member	0	1	25.80	14.42	1.00	14.00	25.00	38.00	51.00	▇▇▆▆▇
promo	0	1	0.33	0.47	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅

moklas$promo<-as.factor(moklas$promo)
moklas$jenis.kelamin<-as.factor(moklas$jenis.kelamin)
moklas$pendidikan<-as.factor(moklas$pendidikan)
moklas$cabang<-as.factor(moklas$cabang)
moklas

summary(moklas)

##      cabang    jenis.kelamin      usia       pendidikan frekuensi.fashion
##  9      : 36   1:153         Min.   :26.00   1: 12      Min.   :0.000    
##  3      : 32   2:206         1st Qu.:37.00   2: 72      1st Qu.:2.000    
##  10     : 32                 Median :40.00   3:156      Median :3.000    
##  7      : 31                 Mean   :40.14   4:119      Mean   :3.061    
##  12     : 29                 3rd Qu.:44.00              3rd Qu.:4.000    
##  14     : 27                 Max.   :56.00              Max.   :8.000    
##  (Other):172                                                             
##  nilai.fashion    frekuensi.footwear nilai.footwear   frekuensi.lainnya
##  Min.   :0.0296   Min.   :0.000      Min.   :0.0250   Min.   :0.000    
##  1st Qu.:0.3944   1st Qu.:2.000      1st Qu.:0.4122   1st Qu.:2.000    
##  Median :0.6694   Median :3.000      Median :0.6946   Median :3.000    
##  Mean   :0.7840   Mean   :3.072      Mean   :0.8474   Mean   :2.783    
##  3rd Qu.:1.0457   3rd Qu.:4.000      3rd Qu.:1.1068   3rd Qu.:4.000    
##  Max.   :2.6276   Max.   :8.000      Max.   :3.5494   Max.   :7.000    
##                                                                        
##  nilai.lainnya    total.nilai.tunai  lama.member   promo  
##  Min.   :0.0214   Min.   : 0.000    Min.   : 1.0   0:239  
##  1st Qu.:0.4332   1st Qu.: 0.000    1st Qu.:14.0   1:120  
##  Median :0.7360   Median : 0.670    Median :25.0          
##  Mean   :0.8527   Mean   : 2.234    Mean   :25.8          
##  3rd Qu.:1.1142   3rd Qu.: 3.040    3rd Qu.:38.0          
##  Max.   :3.0334   Max.   :23.020    Max.   :51.0          
## 

DataExplorer::plot_intro(moklas,theme_config = theme_classic())

cbg<-read.csv("cabang.csv",header=T,sep=';')
cbg

Eksplorasi data

ggplot(cbg, 
       aes(x= Kode.Cabang, 
           y= Banyaknya.Pegawai, fill=Kode.Cabang)) + 
  geom_bar(stat = "identity", 
           fill = rainbow(14), 
           color = "black") +
  geom_col() + 
  labs(title = "Banyaknya Pegawai Tiap Cabang", 
       x = "Kode Cabang", 
       y = "Banyaknya Pegawai", 
       fill = NULL) +
  coord_flip() +
  geom_text(aes(label = cbg$Banyaknya.Pegawai),
            hjust = 2,
            color = "white",
            fontface = "bold")

## Warning: Use of `cbg$Banyaknya.Pegawai` is discouraged.
## ℹ Use `Banyaknya.Pegawai` instead.

# gambar diagram batang dengan variabel y persentasenya, diurutkan secara menurun dan label setiap batang
ggplot(cbg, 
       aes(x = Kode.Cabang,
           y = Banyaknya.Member)) + 
  geom_bar(stat = "identity", 
           fill = rainbow(14), 
           color = "black") +
  geom_text(aes(label = Banyaknya.Member), 
            hjust = -0.10) +
  theme_minimal() +                               
  labs(x = "Kode Cabang", 
       y = "Banyaknya Member", 
       title  = "Banyaknya Member Tiap Cabang")+
  coord_flip()

library(RColorBrewer)
fitur = c("Frekuensi \n Fashion", "Frekuensi \n Footwear", "Frekuensi \n Lainnya")
moklas.frekuensi<-data.frame(moklas$frekuensi.fashion,moklas$frekuensi.footwear,moklas$frekuensi.lainnya)
boxplot(moklas.frekuensi,
        las=1,
        cex = 0.4,
        cex.axis = 0.9,
        cex.names = 1,
        main = "Variabel Penentuan Respon Pelanggan Terhadap Promo",
        ylab="Nilai",
        xlab="",
        horizontal = F,
        col = brewer.pal(8,"Set2"),
        par(mar=c(9,5,4,2)),xaxt="n")
axis(1, at=1:3, labels=fitur, col.axis="black",cex=1.4,cex = 1.4,
        cex.axis = 1)

DataExplorer::plot_bar(data = moklas$promo,ggtheme = theme_bw())

prop.table(table(moklas$promo))

## 
##         0         1 
## 0.6657382 0.3342618

barplot(table(moklas$promo), col = "lightblue")
text(60,"66.57%")
text(2, y = 60,"33.43%")

ggplot(data = moklas, aes(fill = moklas$promo))+
  geom_bar(aes(x = moklas$promo))+
  xlab("")+
  ylab("Banyaknya Pelanggan")+
  scale_y_continuous(expand = c(0,20))+
  scale_x_discrete(expand = c(0,0))+
  theme(legend.position = "none", 
        legend.title = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank())

## Warning: Use of `moklas$promo` is discouraged.
## ℹ Use `promo` instead.
## Use of `moklas$promo` is discouraged.
## ℹ Use `promo` instead.

moklas

library(ggplot2)
p1<- ggplot(data=moklas, aes(y= frekuensi.fashion, group=promo, fill=promo)) +geom_boxplot()
p2<- ggplot(data=moklas, aes(y= frekuensi.footwear, group=promo, fill=promo)) +geom_boxplot()
p3<- ggplot(data=moklas, aes(y= frekuensi.lainnya, group=promo, fill=promo)) +geom_boxplot()
gridExtra::grid.arrange(p1, p2,p3, nrow = 1)

library(ggplot2)
p1<- ggplot(data=moklas, aes(y= nilai.fashion, group=promo, fill=promo)) +geom_boxplot()
p2<- ggplot(data=moklas, aes(y= nilai.footwear, group=promo, fill=promo)) +geom_boxplot()
p3<- ggplot(data=moklas, aes(y= nilai.lainnya, group=promo, fill=promo)) +geom_boxplot()
p4<- ggplot(data=moklas, aes(y= total.nilai.tunai, group=promo, fill=promo)) +geom_boxplot()
gridExtra::grid.arrange(p1, p2,p3,p4, nrow = 1)

library(ggplot2)
p1<- ggplot(data=moklas, aes(y= usia, group=promo, fill=promo)) +geom_boxplot()
p2<- ggplot(data=moklas, aes(y= lama.member, group=promo, fill=promo)) +geom_boxplot()
gridExtra::grid.arrange(p1, p2, nrow = 1)

library(ggplot2)
p1<- ggplot(data=moklas, aes(x= pendidikan, group=promo, fill=promo)) +geom_bar()
p2<- ggplot(data=moklas, aes(x= cabang, group=promo, fill=promo)) +geom_bar()
p3<- ggplot(data=moklas, aes(x= jenis.kelamin, group=promo, fill=promo)) +geom_bar()
gridExtra::grid.arrange(p1, p2,p3, nrow = 1)

library(viridis)

## Warning: package 'viridis' was built under R version 4.3.2

## Loading required package: viridisLite

## 
## Attaching package: 'viridis'

## The following object is masked from 'package:scales':
## 
##     viridis_pal

b1<- ggplot(data=moklas, aes(x= cabang, group=promo, fill=promo)) +
  geom_bar(alpha=.7) +
  scale_fill_viridis(discrete = T)+
  theme_classic()+
  xlab("cabang")
b2<- ggplot(data=moklas, aes(x= jenis.kelamin, group=promo, fill=promo)) +
  geom_bar(alpha=.7) +
  scale_fill_viridis(discrete = T)+
  theme_classic()+
  xlab("jenis kelamin")
b3<- ggplot(data=moklas, aes(x= pendidikan, group=promo, fill=promo)) +
  geom_bar(alpha=.7) +
  scale_fill_viridis(discrete = T)+
  theme_classic()+
  xlab("pendidikan")
gridExtra::grid.arrange(b1,b2,b3, nrow = 1)

ggplot(moklas,aes(x=cabang,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
stat='count',position=position_fill(vjust=0.5),size=2)

## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

ggplot(moklas,aes(x=jenis.kelamin,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
stat='count',position=position_fill(vjust=0.5),size=4)

ggplot(moklas,aes(x=pendidikan,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
stat='count',position=position_fill(vjust=0.5),size=4)

# Mencari korelasi peubah numerik
moklas_numeric <- moklas[sapply(moklas, is.numeric)]
moklas_numeric

corrplot::corrplot(cor(moklas_numeric),
                   method= "number", type = "lower")

plot(moklas$frekuensi.fashion, moklas$frekuensi.lainnya, main = "Scatter Plot Frekuensi Fashion dan Frekuensi Lainnya")

DATA SETELAH DI FEATURE ENGINEERING

setwd("C:/DATA D/S2 Statistika dan Sains Data/Semester 3/Pemodelan Klasifikasi")

moklas1<-read.csv("moklas feature engineering.csv",header = T,sep = ';')
moklas1

moklas1$promo<-as.factor(moklas1$promo)
moklas1$jenis.kelamin<-as.factor(moklas1$jenis.kelamin)
moklas1$pendidikan<-as.factor(moklas1$pendidikan)
moklas1$cabang<-as.factor(moklas1$cabang)
moklas1

Eksplorasi data

DataExplorer::plot_bar(data = moklas1$promo,ggtheme = theme_bw())

prop.table(table(moklas1$promo))

## 
##         0         1 
## 0.6657382 0.3342618

barplot(table(moklas1$promo), col = "lightblue")
text(60,"66.57%")
text(2, y = 60,"33.43%")

moklas1

library(ggplot2)
p1<- ggplot(data=moklas1, aes(y= jumlah.fashion, group=promo, fill=promo)) +geom_boxplot()
p2<- ggplot(data=moklas1, aes(y= jumlah.footwear, group=promo, fill=promo)) +geom_boxplot()
p3<- ggplot(data=moklas1, aes(y= jumlah.lainnya, group=promo, fill=promo)) +geom_boxplot()
gridExtra::grid.arrange(p1, p2,p3, nrow = 1)

library(ggplot2)
p1<- ggplot(data=moklas1, aes(y= usia, group=promo, fill=promo)) +geom_boxplot()
p2<- ggplot(data=moklas1, aes(y= lama.member, group=promo, fill=promo)) +geom_boxplot()
gridExtra::grid.arrange(p1, p2, nrow = 1)

library(ggplot2)
p1<- ggplot(data=moklas1, aes(x= pendidikan, group=promo, fill=promo)) +geom_bar()
p2<- ggplot(data=moklas1, aes(x= cabang, group=promo, fill=promo)) +geom_bar()
p3<- ggplot(data=moklas1, aes(x= jenis.kelamin, group=promo, fill=promo)) +geom_bar()
gridExtra::grid.arrange(p1, p2,p3, nrow = 1)

library(viridis)
b1<- ggplot(data=moklas1, aes(x= cabang, group=promo, fill=promo)) +
  geom_bar(alpha=.7) +
  scale_fill_viridis(discrete = T)+
  theme_classic()+
  xlab("cabang")
b2<- ggplot(data=moklas1, aes(x= jenis.kelamin, group=promo, fill=promo)) +
  geom_bar(alpha=.7) +
  scale_fill_viridis(discrete = T)+
  theme_classic()+
  xlab("jenis kelamin")
b3<- ggplot(data=moklas1, aes(x= pendidikan, group=promo, fill=promo)) +
  geom_bar(alpha=.7) +
  scale_fill_viridis(discrete = T)+
  theme_classic()+
  xlab("pendidikan")
gridExtra::grid.arrange(b1,b2,b3, nrow = 1)

# Mencari korelasi peubah numerik
moklas_numeric <- moklas1[sapply(moklas1, is.numeric)]
moklas_numeric

corrplot::corrplot(cor(moklas_numeric),
                   method= "number", type = "lower")

ggplot(data = moklas1, aes(fill = promo))+
  geom_bar(aes(x = promo))+
  xlab("")+
  ylab("pendidikan")+
  scale_y_continuous(expand = c(0,0))+
  scale_x_discrete(expand = c(0,0))+
  theme(legend.position = "none", 
        legend.title = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank())

ggplot(moklas1,aes(x=pendidikan,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

ggplot(moklas1,aes(x=cabang,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5),cex=2)

ggplot(moklas1,aes(x=jenis.kelamin,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

library(classInt)

## Warning: package 'classInt' was built under R version 4.3.2

# equal frequency discretization
eqfreq<-classIntervals(moklas1$usia, 4, style = 'quantile')
eqfreq$brks<-eqfreq$brks
moklas1$usia<-cut(moklas1$usia, breaks=eqfreq$brks, label=1:4,include.lowest=TRUE)

eqfreq1<-classIntervals(moklas1$jumlah.fashion, 4, style = 'quantile')
eqfreq1$brks

## [1]  0.0000  0.9138  1.8592  3.4946 14.6244

moklas1$jumlah.fashion<-cut(moklas1$jumlah.fashion, breaks=eqfreq1$brks, label=1:4,include.lowest=TRUE)

eqfreq2<-classIntervals(moklas1$jumlah.footwear, 4, style = 'quantile')
eqfreq2$brks

## [1]  0.0000  0.8935  1.8708  3.4200 17.7470

moklas1$jumlah.footwear<-cut(moklas1$jumlah.footwear, breaks=eqfreq2$brks, label=1:4,include.lowest=TRUE)

eqfreq3<-classIntervals(moklas1$jumlah.lainnya, 4, style = 'quantile')
eqfreq3$brks

## [1]  0.0000  0.8029  1.7370  2.9964 14.0660

moklas1$jumlah.lainnya<-cut(moklas1$jumlah.lainnya, breaks=eqfreq3$brks, label=1:4,include.lowest=TRUE)

eqfreq4<-classIntervals(moklas1$total.nilai.tunai, 4, style = 'quantile')
eqfreq4$brks

## [1]  0.00  0.00  0.67  3.04 23.02

moklas1$total.nilai.tunai<-cut(moklas1$total.nilai.tunai, breaks=eqfreq4$brks[-(1)], label=1:3,include.lowest=TRUE)

eqfreq5<-classIntervals(moklas1$lama.member, 4, style = 'quantile')
eqfreq5$brks

## [1]  1 14 25 38 51

moklas1$lama.member<-cut(moklas1$lama.member, breaks=eqfreq5$brks, label=1:4,include.lowest=TRUE)

ggplot(data = moklas1, aes(fill = promo))+
  geom_bar(aes(x = promo))+
  xlab("")+
  ylab("promo")+
  scale_y_continuous(expand = c(0,0))+
  scale_x_discrete(expand = c(0,0))+
  theme(legend.position = "none", 
        legend.title = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank())

moklas1

ggplot(moklas1,aes(x=usia,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

ggplot(moklas1,aes(x=jumlah.fashion,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

ggplot(moklas1,aes(x=jumlah.footwear,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

ggplot(moklas1,aes(x=jumlah.lainnya,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

ggplot(moklas1,aes(x=total.nilai.tunai,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

ggplot(moklas1,aes(x=lama.member,fill=promo))+
    geom_bar(position="fill")+
    geom_text(aes(label=scales::percent(..count../sum(..count..))),
              stat='count',position=position_fill(vjust=0.5))

SPLITTING DATA

set.seed(16)
train3 <- createDataPartition(as.factor(moklas1$promo), p=0.8, list=FALSE)
moklas1.train=moklas1[train3,]
moklas1.test=moklas1[-train3,]

UJI KORELASI

Cramer’s V (Nominal)

thislist <- list()
kolom <- c(1,2)
k = 1

for(i in kolom){
  thislist <- append(thislist,cramerV(moklas1.train[,10], moklas1.train[,i], bias.correct = FALSE)[[1]])
  names(thislist)[[k]] <- names(moklas)[i]
  
  k = k+1
}

thislist

## $cabang
## [1] 0.5056
## 
## $jenis.kelamin
## [1] 0.0644

Kendall (Ordinal)

thislist_2 <- list()
kolom_2 <- 4
k = 1

for(i in kolom_2){
  thislist_2 <- append(thislist_2,cor.test(as.numeric(moklas1.train[,10]), as.numeric(moklas1.train[,i]),method="kendall")$estimate[[1]])
  names(thislist_2)[[k]] <- names(moklas)[i]
  
  k = k+1
}

thislist_2

## $pendidikan
## [1] -0.1015961

IMBALANCE

RANDOM UNDERSAMPLING

set.seed(16)
down_train <- downSample(x = moklas1.train[,-10],y 
= moklas1.train$promo)

colnames(down_train)[colnames(down_train)=="Class"] = "promo"

glimpse(down_train) 

## Rows: 192
## Columns: 10
## $ cabang            <fct> 9, 13, 4, 1, 12, 1, 3, 6, 2, 12, 9, 6, 3, 3, 14, 4, …
## $ jenis.kelamin     <fct> 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2…
## $ usia              <fct> 2, 3, 1, 3, 4, 3, 1, 1, 2, 2, 2, 3, 2, 3, 1, 2, 4, 4…
## $ pendidikan        <fct> 4, 4, 4, 4, 4, 4, 4, 3, 2, 3, 3, 2, 2, 3, 4, 3, 4, 4…
## $ jumlah.fashion    <fct> 2, 2, 2, 1, 1, 4, 4, 1, 3, 4, 4, 3, 1, 1, 1, 1, 4, 4…
## $ jumlah.footwear   <fct> 1, 4, 4, 4, 3, 2, 3, 2, 2, 1, 3, 1, 1, 3, 1, 2, 2, 1…
## $ jumlah.lainnya    <fct> 2, 2, 4, 1, 1, 1, 4, 4, 4, 4, 4, 2, 1, 2, 3, 1, 4, 3…
## $ total.nilai.tunai <fct> 1, 3, 3, 2, 1, 2, 3, 3, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1…
## $ lama.member       <fct> 2, 4, 1, 2, 4, 2, 3, 4, 4, 1, 4, 2, 1, 4, 3, 3, 2, 3…
## $ promo             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

table(moklas1.train$promo)

## 
##   0   1 
## 192  96

table(down_train$promo)

## 
##  0  1 
## 96 96

RANDOM OVERSAMPLING

set.seed(16)
up_train <- upSample(x = moklas1.train,y 
= moklas1.train$promo)

colnames(up_train)[colnames(up_train)=="Class"] = "promo"

glimpse(up_train) 

## Rows: 384
## Columns: 11
## $ cabang            <fct> 9, 14, 11, 9, 1, 10, 5, 1, 10, 13, 8, 10, 14, 11, 1,…
## $ jenis.kelamin     <fct> 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2…
## $ usia              <fct> 1, 3, 2, 4, 3, 4, 3, 3, 4, 3, 2, 2, 1, 3, 3, 3, 2, 3…
## $ pendidikan        <fct> 3, 3, 3, 2, 2, 4, 3, 4, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3…
## $ jumlah.fashion    <fct> 2, 2, 4, 1, 2, 4, 3, 4, 3, 1, 4, 1, 2, 4, 1, 3, 3, 3…
## $ jumlah.footwear   <fct> 4, 2, 1, 1, 1, 1, 2, 2, 2, 3, 4, 3, 4, 4, 4, 2, 4, 2…
## $ jumlah.lainnya    <fct> 4, 2, 4, 1, 3, 2, 4, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2…
## $ total.nilai.tunai <fct> 3, 2, 3, 2, 2, 3, 1, 2, 2, 1, 2, 2, 1, 2, 2, 3, 2, 1…
## $ lama.member       <fct> 1, 4, 4, 1, 1, 2, 4, 2, 3, 3, 3, 3, 3, 2, 2, 2, 4, 2…
## $ promo             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ promo             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

table(moklas1.train$promo)

## 
##   0   1 
## 192  96

table(up_train$promo)

## 
##   0   1 
## 192 192

SMOTE

set.seed(012)
smote_train <- SMOTE(promo ~ ., data = moklas1.train,perc.over=200, perc.under=100)

glimpse(smote_train)

## Rows: 480
## Columns: 10
## $ cabang            <fct> 7, 7, 5, 4, 11, 10, 14, 2, 3, 10, 8, 9, 10, 3, 10, 6…
## $ jenis.kelamin     <fct> 1, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1…
## $ usia              <fct> 1, 2, 1, 3, 3, 2, 3, 2, 1, 2, 3, 1, 1, 3, 4, 1, 2, 4…
## $ pendidikan        <fct> 4, 4, 3, 3, 3, 4, 3, 2, 3, 3, 2, 2, 4, 3, 4, 3, 4, 2…
## $ jumlah.fashion    <fct> 2, 3, 4, 1, 4, 4, 3, 3, 2, 2, 2, 3, 4, 4, 4, 1, 1, 1…
## $ jumlah.footwear   <fct> 4, 1, 3, 1, 4, 1, 3, 2, 1, 4, 1, 4, 2, 4, 1, 2, 2, 1…
## $ jumlah.lainnya    <fct> 1, 2, 1, 2, 1, 4, 3, 4, 4, 3, 4, 3, 4, 4, 2, 4, 4, 1…
## $ total.nilai.tunai <fct> 3, 1, 1, 2, 2, 3, 1, 1, 2, 3, 1, 1, 3, 1, 3, 3, 1, 1…
## $ lama.member       <fct> 3, 2, 1, 1, 2, 3, 2, 4, 2, 1, 1, 3, 3, 3, 2, 4, 4, 1…
## $ promo             <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

table(moklas1.train$promo)

## 
##   0   1 
## 192  96

table(smote_train$promo)

## 
##   0   1 
## 192 288

RANDOM FOREST

## Set seed for reproducibility
set.seed(16)

## Define repeated cross validation with 5 folds and three repeats
repeat_cv <- trainControl(method='repeatedcv', number=5)

## Set seed for reproducibility
set.seed(16)

## Train a random forest model
forest <- train(
        
        # Formula. We are using all variables to predict Species
        promo~., 
        
        # Source of data; remove the Species variable
        data=moklas1.train, 
        
        # `rf` method for random forest
        method='rf', 
        
        # Add repeated cross validation as trControl
        trControl=repeat_cv,
        
        # Accuracy to measure the performance of the model
        metric='Accuracy')

## Print out the details about the model
forest$finalModel

## 
## Call:
##  randomForest(x = x, y = y, mtry = param$mtry) 
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 18
## 
##         OOB estimate of  error rate: 29.17%
## Confusion matrix:
##     0  1 class.error
## 0 161 31   0.1614583
## 1  53 43   0.5520833

#TRAINING DATA
confusionMatrix(forest$trainingData$.outcome,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0  96
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9873, 1)
##     No Information Rate : 0.6667     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.3333     
##          Detection Rate : 0.3333     
##    Detection Prevalence : 0.3333     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats <- predict(
        
        ## Random forest object
        object=forest, 
        
        ## Data to use for predictions; remove the Species
        newdata=moklas1.test[, -10])

#TESTING DATA
confusionMatrix(y_hats,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 41 13
##          1  6 11
##                                           
##                Accuracy : 0.7324          
##                  95% CI : (0.6141, 0.8306)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.1285          
##                                           
##                   Kappa : 0.3561          
##                                           
##  Mcnemar's Test P-Value : 0.1687          
##                                           
##             Sensitivity : 0.4583          
##             Specificity : 0.8723          
##          Pos Pred Value : 0.6471          
##          Neg Pred Value : 0.7593          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1549          
##    Detection Prevalence : 0.2394          
##       Balanced Accuracy : 0.6653          
##                                           
##        'Positive' Class : 1               
## 

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:xgboost':
## 
##     slice

## The following object is masked from 'package:MASS':
## 
##     select

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

set.seed(16)
RSS.test <- c()
ntreesVal = c(50, 100, 200, 300, 400, 500, 600, 700, 800, 900)
moklas1.test$promo <-as.numeric(as.character(moklas1.test$promo))

for (i in ntreesVal) {
    rf.model <- randomForest(promo ~ ., data = moklas1.train, ntree = i)
    rf.test <- predict(rf.model, newdata = moklas1.test[,-10])
    rf.test <-as.numeric(as.character(rf.test))
    RSS.rf <- sum((rf.test-moklas1.test$promo)^2)
    RSS.test <- c(RSS.test, RSS.rf)
}

data <- data.frame(ntreesVal, RSS.test)

fig <- plot_ly(data, x = ntreesVal, y = RSS.test, type = "scatter", mode = "lines")
fig <- fig %>% layout(title = "Fine Tune 'ntree' In Random Forest", xaxis = list(title = "ntree"), 
    yaxis = list(title = "RSS on test set"))

fig

set.seed(16)
RSS.test <- c()
mtryVal = seq(1, 9, by = 1)
moklas1.test$promo <-as.numeric(as.character(moklas1.test$promo))

for (i in mtryVal) {
    rf.model <- randomForest(promo ~ ., data = moklas1.train, ntree = 600, mtry = i)
    rf.test <- predict(rf.model, newdata = moklas1.test[,-10])
    rf.test <-as.numeric(as.character(rf.test))
    RSS.rf <- sum(array(rf.test-moklas1.test$promo)^2)
    RSS.test <- c(RSS.test, RSS.rf)
}

data <- data.frame(mtryVal, RSS.test)

fig <- plot_ly(data, x = ~mtryVal, y = ~RSS.test, type = "scatter", mode = "lines")
fig <- fig %>% layout(title = "Fine Tune 'mtry' In Random Forest", xaxis = list(title = "mtry"), 
    yaxis = list(title = "RSS on test set"))

fig

#Grid Search CV
control <- trainControl(method ="repeatedcv",
                        number = 10,
                        repeats = 3,
                        search = "grid")
set.seed(16)
tunegrid <- expand.grid(.mtry=c(1:10))
rf_gridsearch <- train(promo~.,
                       data = moklas1.train,
                       method = "rf",
                       metric = "Accuracy",
                       tuneGrid = tunegrid,
                       trControl = control,
                       ntree=600)
rf_gridsearch

## Random Forest 
## 
## 288 samples
##   9 predictor
##   2 classes: '0', '1' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times) 
## Summary of sample sizes: 259, 260, 259, 260, 258, 260, ... 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa     
##    1    0.6668172  0.00000000
##    2    0.6796689  0.07817619
##    3    0.7002162  0.19510728
##    4    0.7175014  0.27797695
##    5    0.7278544  0.31305704
##    6    0.7150000  0.28691618
##    7    0.7136864  0.29076193
##    8    0.7206267  0.31376190
##    9    0.7193158  0.31122939
##   10    0.7228489  0.32350760
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 5.

mtry=2

##Modeling Train Data (mtry=2)
library(randomForest)
set.seed(16)
rf_model1<-randomForest(promo~., data = moklas1.train, mtry=2, ntree=600)
rf_model1

## 
## Call:
##  randomForest(formula = promo ~ ., data = moklas1.train, mtry = 2,      ntree = 600) 
##                Type of random forest: classification
##                      Number of trees: 600
## No. of variables tried at each split: 2
## 
##         OOB estimate of  error rate: 27.78%
## Confusion matrix:
##     0  1 class.error
## 0 158 34   0.1770833
## 1  46 50   0.4791667

#TRAINING DATA
pred.rf1 <-as.factor(as.character(rf_model1$predicted))
moklas1.train$promo <-as.factor(as.character(moklas1.train$promo))
confusionMatrix(pred.rf1,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 158  46
##          1  34  50
##                                           
##                Accuracy : 0.7222          
##                  95% CI : (0.6666, 0.7732)
##     No Information Rate : 0.6667          
##     P-Value [Acc > NIR] : 0.02505         
##                                           
##                   Kappa : 0.3548          
##                                           
##  Mcnemar's Test P-Value : 0.21876         
##                                           
##             Sensitivity : 0.5208          
##             Specificity : 0.8229          
##          Pos Pred Value : 0.5952          
##          Neg Pred Value : 0.7745          
##              Prevalence : 0.3333          
##          Detection Rate : 0.1736          
##    Detection Prevalence : 0.2917          
##       Balanced Accuracy : 0.6719          
##                                           
##        'Positive' Class : 1               
## 

## Generate predictions
y_hats1 <- predict(
        
        ## Random forest object
        object=rf_model1, 
        
        ## Data to use for predictions; remove the Species
        newdata=moklas1.test[, -10])

#TESTING DATA
y_hats1 <-as.factor(as.character(y_hats1))
moklas1.test$promo <-as.factor(as.character(moklas1.test$promo))
confusionMatrix(y_hats1,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 44 12
##          1  3 12
##                                           
##                Accuracy : 0.7887          
##                  95% CI : (0.6756, 0.8767)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.01404         
##                                           
##                   Kappa : 0.4802          
##                                           
##  Mcnemar's Test P-Value : 0.03887         
##                                           
##             Sensitivity : 0.5000          
##             Specificity : 0.9362          
##          Pos Pred Value : 0.8000          
##          Neg Pred Value : 0.7857          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1690          
##    Detection Prevalence : 0.2113          
##       Balanced Accuracy : 0.7181          
##                                           
##        'Positive' Class : 1               
## 

mtry=3

##Modeling Train Data (mtry=3)
library(randomForest)
set.seed(16)
rf_model2<-randomForest(promo~., data = moklas1.train, mtry=3, ntree=600)
rf_model2

## 
## Call:
##  randomForest(formula = promo ~ ., data = moklas1.train, mtry = 3,      ntree = 600) 
##                Type of random forest: classification
##                      Number of trees: 600
## No. of variables tried at each split: 3
## 
##         OOB estimate of  error rate: 29.51%
## Confusion matrix:
##     0  1 class.error
## 0 156 36   0.1875000
## 1  49 47   0.5104167

#TRAINING DATA
pred.rf2 <-as.factor(as.character(rf_model2$predicted))
moklas1.train$promo <-as.factor(as.character(moklas1.train$promo))
confusionMatrix(pred.rf2,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 156  49
##          1  36  47
##                                           
##                Accuracy : 0.7049          
##                  95% CI : (0.6485, 0.7569)
##     No Information Rate : 0.6667          
##     P-Value [Acc > NIR] : 0.09375         
##                                           
##                   Kappa : 0.3127          
##                                           
##  Mcnemar's Test P-Value : 0.19306         
##                                           
##             Sensitivity : 0.4896          
##             Specificity : 0.8125          
##          Pos Pred Value : 0.5663          
##          Neg Pred Value : 0.7610          
##              Prevalence : 0.3333          
##          Detection Rate : 0.1632          
##    Detection Prevalence : 0.2882          
##       Balanced Accuracy : 0.6510          
##                                           
##        'Positive' Class : 1               
## 

## Generate predictions
y_hats2 <- predict(
        
        ## Random forest object
        object=rf_model2, 
        
        ## Data to use for predictions; remove the Species
        newdata=moklas1.test[, -10])

#TESTING DATA
y_hats2 <-as.factor(as.character(y_hats2))
moklas1.test$promo <-as.factor(as.character(moklas1.test$promo))
confusionMatrix(y_hats2,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 42 10
##          1  5 14
##                                           
##                Accuracy : 0.7887          
##                  95% CI : (0.6756, 0.8767)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.01404         
##                                           
##                   Kappa : 0.5026          
##                                           
##  Mcnemar's Test P-Value : 0.30170         
##                                           
##             Sensitivity : 0.5833          
##             Specificity : 0.8936          
##          Pos Pred Value : 0.7368          
##          Neg Pred Value : 0.8077          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1972          
##    Detection Prevalence : 0.2676          
##       Balanced Accuracy : 0.7385          
##                                           
##        'Positive' Class : 1               
## 

mtry=4

##Modeling Train Data (mtry=4)
library(randomForest)
set.seed(16)
rf_model3<-randomForest(promo~., data = moklas1.train, mtry=4, ntree=600)
rf_model3

## 
## Call:
##  randomForest(formula = promo ~ ., data = moklas1.train, mtry = 4,      ntree = 600) 
##                Type of random forest: classification
##                      Number of trees: 600
## No. of variables tried at each split: 4
## 
##         OOB estimate of  error rate: 28.82%
## Confusion matrix:
##     0  1 class.error
## 0 156 36   0.1875000
## 1  47 49   0.4895833

#TRAINING DATA
pred.rf3 <-as.factor(as.character(rf_model3$predicted))
moklas1.train$promo <-as.factor(as.character(moklas1.train$promo))
confusionMatrix(pred.rf3,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 156  47
##          1  36  49
##                                           
##                Accuracy : 0.7118          
##                  95% CI : (0.6558, 0.7634)
##     No Information Rate : 0.6667          
##     P-Value [Acc > NIR] : 0.05779         
##                                           
##                   Kappa : 0.3324          
##                                           
##  Mcnemar's Test P-Value : 0.27236         
##                                           
##             Sensitivity : 0.5104          
##             Specificity : 0.8125          
##          Pos Pred Value : 0.5765          
##          Neg Pred Value : 0.7685          
##              Prevalence : 0.3333          
##          Detection Rate : 0.1701          
##    Detection Prevalence : 0.2951          
##       Balanced Accuracy : 0.6615          
##                                           
##        'Positive' Class : 1               
## 

## Generate predictions
y_hats3 <- predict(
        
        ## Random forest object
        object=rf_model3, 
        
        ## Data to use for predictions; remove the Species
        newdata=moklas1.test[, -10])

#TESTING DATA
y_hats3 <-as.factor(as.character(y_hats3))
moklas1.test$promo <-as.factor(as.character(moklas1.test$promo))
confusionMatrix(y_hats3,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 43 10
##          1  4 14
##                                           
##                Accuracy : 0.8028          
##                  95% CI : (0.6914, 0.8878)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.006749        
##                                           
##                   Kappa : 0.5307          
##                                           
##  Mcnemar's Test P-Value : 0.181449        
##                                           
##             Sensitivity : 0.5833          
##             Specificity : 0.9149          
##          Pos Pred Value : 0.7778          
##          Neg Pred Value : 0.8113          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1972          
##    Detection Prevalence : 0.2535          
##       Balanced Accuracy : 0.7491          
##                                           
##        'Positive' Class : 1               
## 

varImpPlot(rf_model3)

RUS

##Modeling Train Data (mtry=4)
library(randomForest)
set.seed(16)
rf_model4<-randomForest(promo~., data = down_train, mtry=4, ntree=600)
rf_model4

## 
## Call:
##  randomForest(formula = promo ~ ., data = down_train, mtry = 4,      ntree = 600) 
##                Type of random forest: classification
##                      Number of trees: 600
## No. of variables tried at each split: 4
## 
##         OOB estimate of  error rate: 30.21%
## Confusion matrix:
##    0  1 class.error
## 0 59 37   0.3854167
## 1 21 75   0.2187500

#TRAINING DATA
pred.rf4 <-as.factor(as.character(rf_model4$predicted))
down_train$promo <-as.factor(as.character(down_train$promo))
confusionMatrix(pred.rf4,down_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 59 21
##          1 37 75
##                                           
##                Accuracy : 0.6979          
##                  95% CI : (0.6277, 0.7619)
##     No Information Rate : 0.5             
##     P-Value [Acc > NIR] : 2.09e-08        
##                                           
##                   Kappa : 0.3958          
##                                           
##  Mcnemar's Test P-Value : 0.04888         
##                                           
##             Sensitivity : 0.7812          
##             Specificity : 0.6146          
##          Pos Pred Value : 0.6696          
##          Neg Pred Value : 0.7375          
##              Prevalence : 0.5000          
##          Detection Rate : 0.3906          
##    Detection Prevalence : 0.5833          
##       Balanced Accuracy : 0.6979          
##                                           
##        'Positive' Class : 1               
## 

## Generate predictions
y_hats4 <- predict(
        
        ## Random forest object
        object=rf_model4, 
        
        ## Data to use for predictions; remove the Species
        newdata=moklas1.test[, -10])

#TESTING DATA
y_hats4 <-as.factor(as.character(y_hats4))
moklas1.test$promo <-as.factor(as.character(moklas1.test$promo))
confusionMatrix(y_hats4,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 32  7
##          1 15 17
##                                           
##                Accuracy : 0.6901          
##                  95% CI : (0.5692, 0.7946)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.3579          
##                                           
##                   Kappa : 0.3598          
##                                           
##  Mcnemar's Test P-Value : 0.1356          
##                                           
##             Sensitivity : 0.7083          
##             Specificity : 0.6809          
##          Pos Pred Value : 0.5312          
##          Neg Pred Value : 0.8205          
##              Prevalence : 0.3380          
##          Detection Rate : 0.2394          
##    Detection Prevalence : 0.4507          
##       Balanced Accuracy : 0.6946          
##                                           
##        'Positive' Class : 1               
## 

ROS

##Modeling Train Data (mtry=4)
library(randomForest)
set.seed(16)
rf_model5<-randomForest(promo~., data = up_train, mtry=4, ntree=600)
rf_model5

## 
## Call:
##  randomForest(formula = promo ~ ., data = up_train, mtry = 4,      ntree = 600) 
##                Type of random forest: classification
##                      Number of trees: 600
## No. of variables tried at each split: 4
## 
##         OOB estimate of  error rate: 0%
## Confusion matrix:
##     0   1 class.error
## 0 192   0           0
## 1   0 192           0

#TRAINING DATA
pred.rf5 <-as.factor(as.character(rf_model5$predicted))
up_train$promo <-as.factor(as.character(up_train$promo))
confusionMatrix(pred.rf5,up_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0 192
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9904, 1)
##     No Information Rate : 0.5        
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0        
##             Specificity : 1.0        
##          Pos Pred Value : 1.0        
##          Neg Pred Value : 1.0        
##              Prevalence : 0.5        
##          Detection Rate : 0.5        
##    Detection Prevalence : 0.5        
##       Balanced Accuracy : 1.0        
##                                      
##        'Positive' Class : 1          
## 

SMOTE

##Modeling Train Data (mtry=4)
library(randomForest)
set.seed(16)
rf_model6<-randomForest(promo~., data = smote_train, mtry=4, ntree=600)
rf_model6

## 
## Call:
##  randomForest(formula = promo ~ ., data = smote_train, mtry = 4,      ntree = 600) 
##                Type of random forest: classification
##                      Number of trees: 600
## No. of variables tried at each split: 4
## 
##         OOB estimate of  error rate: 12.29%
## Confusion matrix:
##     0   1 class.error
## 0 155  37  0.19270833
## 1  22 266  0.07638889

#TRAINING DATA
pred.rf6 <-as.factor(as.character(rf_model6$predicted))
smote_train$promo <-as.factor(as.character(smote_train$promo))
confusionMatrix(pred.rf6,smote_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 155  22
##          1  37 266
##                                           
##                Accuracy : 0.8771          
##                  95% CI : (0.8443, 0.9051)
##     No Information Rate : 0.6             
##     P-Value [Acc > NIR] : < 2e-16         
##                                           
##                   Kappa : 0.7405          
##                                           
##  Mcnemar's Test P-Value : 0.06836         
##                                           
##             Sensitivity : 0.9236          
##             Specificity : 0.8073          
##          Pos Pred Value : 0.8779          
##          Neg Pred Value : 0.8757          
##              Prevalence : 0.6000          
##          Detection Rate : 0.5542          
##    Detection Prevalence : 0.6312          
##       Balanced Accuracy : 0.8655          
##                                           
##        'Positive' Class : 1               
## 

## Generate predictions
y_hats6 <- predict(
        
        ## Random forest object
        object=rf_model6, 
        
        ## Data to use for predictions; remove the Species
        newdata=moklas1.test[, -10])

#TESTING DATA
y_hats6 <-as.factor(as.character(y_hats6))
moklas1.test$promo <-as.factor(as.character(moklas1.test$promo))
confusionMatrix(y_hats6,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 29  4
##          1 18 20
##                                           
##                Accuracy : 0.6901          
##                  95% CI : (0.5692, 0.7946)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.357869        
##                                           
##                   Kappa : 0.3941          
##                                           
##  Mcnemar's Test P-Value : 0.005578        
##                                           
##             Sensitivity : 0.8333          
##             Specificity : 0.6170          
##          Pos Pred Value : 0.5263          
##          Neg Pred Value : 0.8788          
##              Prevalence : 0.3380          
##          Detection Rate : 0.2817          
##    Detection Prevalence : 0.5352          
##       Balanced Accuracy : 0.7252          
##                                           
##        'Positive' Class : 1               
## 

EXTRA TREES

## Set seed for reproducibility
set.seed(16)

## Define repeated cross validation with 5 folds and three repeats
repeat_cv <- trainControl(method='repeatedcv', number=5)

## Set seed for reproducibility
set.seed(16)

## Train a random forest model
extraT <- train(
  
  # Formula. We are using all variables to predict Species
  promo~.,
  tuneGrid = data.frame(mtry = c(1,2,3,4,5,6,7,8,9,10),
                        splitrule = c("extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees"),min.node.size = 10L),
  
  # Source of data; remove the Species variable
  data=moklas1.train, 
  
  # `rf` method for random forest
  method='ranger', 
  
  # Add repeated cross validation as trControl
  trControl=repeat_cv
  )

## Print out the details about the model
extraT$finalModel

## Ranger result
## 
## Call:
##  ranger::ranger(dependent.variable.name = ".outcome", data = x,      mtry = min(param$mtry, ncol(x)), min.node.size = param$min.node.size,      splitrule = as.character(param$splitrule), write.forest = TRUE,      probability = classProbs, ...) 
## 
## Type:                             Classification 
## Number of trees:                  500 
## Sample size:                      288 
## Number of independent variables:  34 
## Mtry:                             6 
## Target node size:                 10 
## Variable importance mode:         none 
## Splitrule:                        extratrees 
## Number of random splits:          1 
## OOB prediction error:             29.17 %

extraT$trainingData

extraT$bestTune

confusionMatrix(extraT$trainingData$.outcome,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0  96
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9873, 1)
##     No Information Rate : 0.6667     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.3333     
##          Detection Rate : 0.3333     
##    Detection Prevalence : 0.3333     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_e1 <- predict(
  
  ## Random forest object
  object=extraT, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_e1,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 45 13
##          1  2 11
##                                           
##                Accuracy : 0.7887          
##                  95% CI : (0.6756, 0.8767)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.014037        
##                                           
##                   Kappa : 0.4683          
##                                           
##  Mcnemar's Test P-Value : 0.009823        
##                                           
##             Sensitivity : 0.4583          
##             Specificity : 0.9574          
##          Pos Pred Value : 0.8462          
##          Neg Pred Value : 0.7759          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1549          
##    Detection Prevalence : 0.1831          
##       Balanced Accuracy : 0.7079          
##                                           
##        'Positive' Class : 1               
## 

## Set seed for reproducibility
set.seed(16)

## Define repeated cross validation with 5 folds and three repeats
repeat_cv <- trainControl(method='repeatedcv', number=5)
extra.best<-extraT$bestTune

## Set seed for reproducibility
set.seed(16)

## Train a random forest model
extraT.g <- train(
  
  # Formula. We are using all variables to predict Species
  promo~.,
  tuneGrid = extra.best,
  data=moklas1.train, 
  method='ranger', 
  trControl=repeat_cv
  )

## Print out the details about the model
extraT.g$finalModel

## Ranger result
## 
## Call:
##  ranger::ranger(dependent.variable.name = ".outcome", data = x,      mtry = min(param$mtry, ncol(x)), min.node.size = param$min.node.size,      splitrule = as.character(param$splitrule), write.forest = TRUE,      probability = classProbs, ...) 
## 
## Type:                             Classification 
## Number of trees:                  500 
## Sample size:                      288 
## Number of independent variables:  34 
## Mtry:                             6 
## Target node size:                 10 
## Variable importance mode:         none 
## Splitrule:                        extratrees 
## Number of random splits:          1 
## OOB prediction error:             27.43 %

confusionMatrix(extraT.g$trainingData$.outcome,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0  96
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9873, 1)
##     No Information Rate : 0.6667     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.3333     
##          Detection Rate : 0.3333     
##    Detection Prevalence : 0.3333     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_e1.g <- predict(
  
  ## Random forest object
  object=extraT.g, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_e1.g,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 46 14
##          1  1 10
##                                           
##                Accuracy : 0.7887          
##                  95% CI : (0.6756, 0.8767)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.014037        
##                                           
##                   Kappa : 0.4558          
##                                           
##  Mcnemar's Test P-Value : 0.001946        
##                                           
##             Sensitivity : 0.4167          
##             Specificity : 0.9787          
##          Pos Pred Value : 0.9091          
##          Neg Pred Value : 0.7667          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1408          
##    Detection Prevalence : 0.1549          
##       Balanced Accuracy : 0.6977          
##                                           
##        'Positive' Class : 1               
## 

RUS

## Set seed for reproducibility
set.seed(16)
repeat_cv <- trainControl(method='repeatedcv', number=5)

set.seed(16)
extraT1 <- train(
  promo~.,
  tuneGrid = data.frame(mtry = c(1,2,3,4,5,6,7,8,9,10),
                        splitrule = c("extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees"),min.node.size = 10L),
  data=down_train, 
  method='ranger', 
  trControl=repeat_cv
  )

## Print out the details about the model
extraT1$finalModel

## Ranger result
## 
## Call:
##  ranger::ranger(dependent.variable.name = ".outcome", data = x,      mtry = min(param$mtry, ncol(x)), min.node.size = param$min.node.size,      splitrule = as.character(param$splitrule), write.forest = TRUE,      probability = classProbs, ...) 
## 
## Type:                             Classification 
## Number of trees:                  500 
## Sample size:                      192 
## Number of independent variables:  34 
## Mtry:                             4 
## Target node size:                 10 
## Variable importance mode:         none 
## Splitrule:                        extratrees 
## Number of random splits:          1 
## OOB prediction error:             33.85 %

confusionMatrix(extraT1$trainingData$.outcome,down_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 96  0
##          1  0 96
##                                     
##                Accuracy : 1         
##                  95% CI : (0.981, 1)
##     No Information Rate : 0.5       
##     P-Value [Acc > NIR] : < 2.2e-16 
##                                     
##                   Kappa : 1         
##                                     
##  Mcnemar's Test P-Value : NA        
##                                     
##             Sensitivity : 1.0       
##             Specificity : 1.0       
##          Pos Pred Value : 1.0       
##          Neg Pred Value : 1.0       
##              Prevalence : 0.5       
##          Detection Rate : 0.5       
##    Detection Prevalence : 0.5       
##       Balanced Accuracy : 1.0       
##                                     
##        'Positive' Class : 1         
## 

## Generate predictions
y_hats_e2 <- predict(
  
  ## Random forest object
  object=extraT1, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_e2,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 36  9
##          1 11 15
##                                          
##                Accuracy : 0.7183         
##                  95% CI : (0.599, 0.8187)
##     No Information Rate : 0.662          
##     P-Value [Acc > NIR] : 0.1908         
##                                          
##                   Kappa : 0.3831         
##                                          
##  Mcnemar's Test P-Value : 0.8231         
##                                          
##             Sensitivity : 0.6250         
##             Specificity : 0.7660         
##          Pos Pred Value : 0.5769         
##          Neg Pred Value : 0.8000         
##              Prevalence : 0.3380         
##          Detection Rate : 0.2113         
##    Detection Prevalence : 0.3662         
##       Balanced Accuracy : 0.6955         
##                                          
##        'Positive' Class : 1              
## 

ROS

## Set seed for reproducibility
set.seed(16)
repeat_cv <- trainControl(method='repeatedcv', number=5)

set.seed(16)
extraT2 <- train(
  promo~.,
  tuneGrid = data.frame(mtry = c(1,2,3,4,5,6,7,8,9,10),
                        splitrule = c("extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees"),min.node.size = 10L),
  data=up_train, 
  method='ranger', 
  trControl=repeat_cv
  )

## Warning in model.matrix.default(Terms, m, contrasts): the response appeared on
## the right-hand side and was dropped

## Warning in model.matrix.default(Terms, m, contrasts): problem with term 10 in
## model.matrix: no columns are assigned

## Print out the details about the model
extraT2$finalModel

## Ranger result
## 
## Call:
##  ranger::ranger(dependent.variable.name = ".outcome", data = x,      mtry = min(param$mtry, ncol(x)), min.node.size = param$min.node.size,      splitrule = as.character(param$splitrule), write.forest = TRUE,      probability = classProbs, ...) 
## 
## Type:                             Classification 
## Number of trees:                  500 
## Sample size:                      384 
## Number of independent variables:  34 
## Mtry:                             6 
## Target node size:                 10 
## Variable importance mode:         none 
## Splitrule:                        extratrees 
## Number of random splits:          1 
## OOB prediction error:             16.93 %

confusionMatrix(extraT2$trainingData$.outcome,up_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0 192
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9904, 1)
##     No Information Rate : 0.5        
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0        
##             Specificity : 1.0        
##          Pos Pred Value : 1.0        
##          Neg Pred Value : 1.0        
##              Prevalence : 0.5        
##          Detection Rate : 0.5        
##    Detection Prevalence : 0.5        
##       Balanced Accuracy : 1.0        
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_e3 <- predict(
  
  ## Random forest object
  object=extraT2, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_e3,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 41 10
##          1  6 14
##                                         
##                Accuracy : 0.7746        
##                  95% CI : (0.66, 0.8654)
##     No Information Rate : 0.662         
##     P-Value [Acc > NIR] : 0.02706       
##                                         
##                   Kappa : 0.475         
##                                         
##  Mcnemar's Test P-Value : 0.45325       
##                                         
##             Sensitivity : 0.5833        
##             Specificity : 0.8723        
##          Pos Pred Value : 0.7000        
##          Neg Pred Value : 0.8039        
##              Prevalence : 0.3380        
##          Detection Rate : 0.1972        
##    Detection Prevalence : 0.2817        
##       Balanced Accuracy : 0.7278        
##                                         
##        'Positive' Class : 1             
## 

SMOTE

## Set seed for reproducibility
set.seed(16)
repeat_cv <- trainControl(method='repeatedcv', number=5)

set.seed(16)
extraT3 <- train(
  promo~.,
  tuneGrid = data.frame(mtry = c(1,2,3,4,5,6,7,8,9,10),
                        splitrule = c("extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees","extratrees"),min.node.size = 10L),
  data=smote_train, 
  method='ranger', 
  trControl=repeat_cv
  )

## Print out the details about the model
extraT3$finalModel

## Ranger result
## 
## Call:
##  ranger::ranger(dependent.variable.name = ".outcome", data = x,      mtry = min(param$mtry, ncol(x)), min.node.size = param$min.node.size,      splitrule = as.character(param$splitrule), write.forest = TRUE,      probability = classProbs, ...) 
## 
## Type:                             Classification 
## Number of trees:                  500 
## Sample size:                      480 
## Number of independent variables:  34 
## Mtry:                             6 
## Target node size:                 10 
## Variable importance mode:         none 
## Splitrule:                        extratrees 
## Number of random splits:          1 
## OOB prediction error:             16.25 %

confusionMatrix(extraT3$trainingData$.outcome,smote_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0 288
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9923, 1)
##     No Information Rate : 0.6        
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0        
##             Specificity : 1.0        
##          Pos Pred Value : 1.0        
##          Neg Pred Value : 1.0        
##              Prevalence : 0.6        
##          Detection Rate : 0.6        
##    Detection Prevalence : 0.6        
##       Balanced Accuracy : 1.0        
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_e4 <- predict(
  
  ## Random forest object
  object=extraT3, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_e4,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 28  5
##          1 19 19
##                                         
##                Accuracy : 0.662         
##                  95% CI : (0.5399, 0.77)
##     No Information Rate : 0.662         
##     P-Value [Acc > NIR] : 0.555156      
##                                         
##                   Kappa : 0.339         
##                                         
##  Mcnemar's Test P-Value : 0.007963      
##                                         
##             Sensitivity : 0.7917        
##             Specificity : 0.5957        
##          Pos Pred Value : 0.5000        
##          Neg Pred Value : 0.8485        
##              Prevalence : 0.3380        
##          Detection Rate : 0.2676        
##    Detection Prevalence : 0.5352        
##       Balanced Accuracy : 0.6937        
##                                         
##        'Positive' Class : 1             
## 

GRADIENT BOOSTING

## Set seed for reproducibility
set.seed(16)

## Define repeated cross validation with 5 folds and three repeats
repeat_cv <- trainControl(method='repeatedcv', number=5)

boost_1 <- train(promo ~., 
               data=moklas1.train, 
               method="gbm",
               tuneLength = 5,  
               trControl=repeat_cv,
               verbose = FALSE)

plot(boost_1, main = "5-Fold Cross Validation Gradient Boosting: tuneLength")

boost_best <- boost_1$bestTune

boost_1 <- train(promo ~., 
               data=moklas1.train, 
               method="gbm",
               tuneGrid  = boost_best,
               verbose = FALSE)

boost_result_1 <- boost_1$results
boost_result_1

boost_1$trainingData

confusionMatrix(boost_1$trainingData$.outcome,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0  96
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9873, 1)
##     No Information Rate : 0.6667     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.3333     
##          Detection Rate : 0.3333     
##    Detection Prevalence : 0.3333     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_b1 <- predict(
  
  ## Random forest object
  object=boost_1, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_b1,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 45 13
##          1  2 11
##                                           
##                Accuracy : 0.7887          
##                  95% CI : (0.6756, 0.8767)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.014037        
##                                           
##                   Kappa : 0.4683          
##                                           
##  Mcnemar's Test P-Value : 0.009823        
##                                           
##             Sensitivity : 0.4583          
##             Specificity : 0.9574          
##          Pos Pred Value : 0.8462          
##          Neg Pred Value : 0.7759          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1549          
##    Detection Prevalence : 0.1831          
##       Balanced Accuracy : 0.7079          
##                                           
##        'Positive' Class : 1               
## 

RUS

boost_2 <- train(promo ~., 
               data=down_train, 
               method="gbm",
               tuneLength = 5,  
               trControl=repeat_cv,
               verbose = FALSE)

confusionMatrix(boost_2$trainingData$.outcome,down_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 96  0
##          1  0 96
##                                     
##                Accuracy : 1         
##                  95% CI : (0.981, 1)
##     No Information Rate : 0.5       
##     P-Value [Acc > NIR] : < 2.2e-16 
##                                     
##                   Kappa : 1         
##                                     
##  Mcnemar's Test P-Value : NA        
##                                     
##             Sensitivity : 1.0       
##             Specificity : 1.0       
##          Pos Pred Value : 1.0       
##          Neg Pred Value : 1.0       
##              Prevalence : 0.5       
##          Detection Rate : 0.5       
##    Detection Prevalence : 0.5       
##       Balanced Accuracy : 1.0       
##                                     
##        'Positive' Class : 1         
## 

## Generate predictions
y_hats_b2 <- predict(
  
  ## Random forest object
  object=boost_2, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_b2,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 35 11
##          1 12 13
##                                           
##                Accuracy : 0.6761          
##                  95% CI : (0.5545, 0.7824)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.4555          
##                                           
##                   Kappa : 0.2835          
##                                           
##  Mcnemar's Test P-Value : 1.0000          
##                                           
##             Sensitivity : 0.5417          
##             Specificity : 0.7447          
##          Pos Pred Value : 0.5200          
##          Neg Pred Value : 0.7609          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1831          
##    Detection Prevalence : 0.3521          
##       Balanced Accuracy : 0.6432          
##                                           
##        'Positive' Class : 1               
## 

ROS

boost_3 <- train(promo ~., 
               data=up_train, 
               method="gbm",
               tuneLength = 5,  
               trControl=repeat_cv,
               verbose = FALSE)

## Warning in model.matrix.default(Terms, m, contrasts): the response appeared on
## the right-hand side and was dropped

## Warning in model.matrix.default(Terms, m, contrasts): problem with term 10 in
## model.matrix: no columns are assigned

confusionMatrix(boost_3$trainingData$.outcome,up_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0 192
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9904, 1)
##     No Information Rate : 0.5        
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0        
##             Specificity : 1.0        
##          Pos Pred Value : 1.0        
##          Neg Pred Value : 1.0        
##              Prevalence : 0.5        
##          Detection Rate : 0.5        
##    Detection Prevalence : 0.5        
##       Balanced Accuracy : 1.0        
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_b3 <- predict(
  
  ## Random forest object
  object=boost_3, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_b3,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 38 10
##          1  9 14
##                                           
##                Accuracy : 0.7324          
##                  95% CI : (0.6141, 0.8306)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.1285          
##                                           
##                   Kappa : 0.3959          
##                                           
##  Mcnemar's Test P-Value : 1.0000          
##                                           
##             Sensitivity : 0.5833          
##             Specificity : 0.8085          
##          Pos Pred Value : 0.6087          
##          Neg Pred Value : 0.7917          
##              Prevalence : 0.3380          
##          Detection Rate : 0.1972          
##    Detection Prevalence : 0.3239          
##       Balanced Accuracy : 0.6959          
##                                           
##        'Positive' Class : 1               
## 

SMOTE

boost_4 <- train(promo ~., 
               data=smote_train, 
               method="gbm",
               tuneLength = 5,  
               trControl=repeat_cv,
               verbose = FALSE)

confusionMatrix(boost_4$trainingData$.outcome,smote_train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0 288
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9923, 1)
##     No Information Rate : 0.6        
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0        
##             Specificity : 1.0        
##          Pos Pred Value : 1.0        
##          Neg Pred Value : 1.0        
##              Prevalence : 0.6        
##          Detection Rate : 0.6        
##    Detection Prevalence : 0.6        
##       Balanced Accuracy : 1.0        
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_b4 <- predict(
  
  ## Random forest object
  object=boost_4, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_b4,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 33  6
##          1 14 18
##                                          
##                Accuracy : 0.7183         
##                  95% CI : (0.599, 0.8187)
##     No Information Rate : 0.662          
##     P-Value [Acc > NIR] : 0.1908         
##                                          
##                   Kappa : 0.418          
##                                          
##  Mcnemar's Test P-Value : 0.1175         
##                                          
##             Sensitivity : 0.7500         
##             Specificity : 0.7021         
##          Pos Pred Value : 0.5625         
##          Neg Pred Value : 0.8462         
##              Prevalence : 0.3380         
##          Detection Rate : 0.2535         
##    Detection Prevalence : 0.4507         
##       Balanced Accuracy : 0.7261         
##                                          
##        'Positive' Class : 1              
## 

XTREME GRADIENT BOOSTING

# Basic Parameter Tuning
fitControl <- trainControl(## 5-fold CV
                           method = "repeatedcv",
                           number = 5,
                           ## repeated ten times
                           repeats = 5)

# Alternate Tuning Grids
xgbGrid <-  expand.grid(nrounds = c(300, 500, 1000, 1500),
                        max_depth = 2,
                        eta = c(0.01, 0.02, 0.03),
                        gamma = 0,
                        colsample_bytree = 1,
                        min_child_weight = 1,
                        subsample = 1
                        )

set.seed(16)
xgbFit <- train(promo~ ., data = moklas1.train, 
                 method = "xgbTree", 
                 trControl = fitControl,
                 verbose = FALSE,
                 tuneGrid = xgbGrid,
                 objective="reg:squarederror")

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:28:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:28:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:29:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:29:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## [23:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [23:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

xgbFit

## eXtreme Gradient Boosting 
## 
## 288 samples
##   9 predictor
##   2 classes: '0', '1' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times) 
## Summary of sample sizes: 231, 231, 231, 230, 229, 231, ... 
## Resampling results across tuning parameters:
## 
##   eta   nrounds  Accuracy   Kappa    
##   0.01   300     0.7319280  0.3080092
##   0.01   500     0.7298820  0.3220357
##   0.01  1000     0.7250419  0.3287622
##   0.01  1500     0.7167535  0.3162639
##   0.02   300     0.7270875  0.3205561
##   0.02   500     0.7250661  0.3262722
##   0.02  1000     0.7042425  0.2924492
##   0.02  1500     0.6965728  0.2841711
##   0.03   300     0.7243289  0.3219649
##   0.03   500     0.7174553  0.3166680
##   0.03  1000     0.6972988  0.2865659
##   0.03  1500     0.6861675  0.2636921
## 
## Tuning parameter 'max_depth' was held constant at a value of 2
## Tuning
## 
## Tuning parameter 'min_child_weight' was held constant at a value of 1
## 
## Tuning parameter 'subsample' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were nrounds = 300, max_depth = 2, eta
##  = 0.01, gamma = 0, colsample_bytree = 1, min_child_weight = 1 and subsample
##  = 1.

xgbFit$bestTune

xgbFit.best<-xgbFit$bestTune
set.seed(16)
xgbFit1 <- train(promo~ ., data = moklas1.train, 
                 method = "xgbTree", 
                 trControl = fitControl,
                 verbose = FALSE,
                 tuneGrid = xgbFit.best,
                 objective="reg:squarederror")

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

## Warning in check.booster.params(params, ...): The following parameters were provided multiple times:
##  objective
##   Only the last value for each of them will be used.

xgbFit1

## eXtreme Gradient Boosting 
## 
## 288 samples
##   9 predictor
##   2 classes: '0', '1' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times) 
## Summary of sample sizes: 231, 231, 231, 230, 229, 231, ... 
## Resampling results:
## 
##   Accuracy  Kappa    
##   0.731928  0.3080092
## 
## Tuning parameter 'nrounds' was held constant at a value of 300
## Tuning
##  held constant at a value of 1
## Tuning parameter 'subsample' was held
##  constant at a value of 1

confusionMatrix(xgbFit1$trainingData$.outcome,moklas1.train$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 192   0
##          1   0  96
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9873, 1)
##     No Information Rate : 0.6667     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.3333     
##          Detection Rate : 0.3333     
##    Detection Prevalence : 0.3333     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : 1          
## 

## Generate predictions
y_hats_x1 <- predict(
  
  ## Random forest object
  object=xgbFit1, 
  
  ## Data to use for predictions; remove the Species
  newdata=moklas1.test[, -10])

confusionMatrix(y_hats_x1,moklas1.test$promo, positive="1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 44 13
##          1  3 11
##                                         
##                Accuracy : 0.7746        
##                  95% CI : (0.66, 0.8654)
##     No Information Rate : 0.662         
##     P-Value [Acc > NIR] : 0.02706       
##                                         
##                   Kappa : 0.4393        
##                                         
##  Mcnemar's Test P-Value : 0.02445       
##                                         
##             Sensitivity : 0.4583        
##             Specificity : 0.9362        
##          Pos Pred Value : 0.7857        
##          Neg Pred Value : 0.7719        
##              Prevalence : 0.3380        
##          Detection Rate : 0.1549        
##    Detection Prevalence : 0.1972        
##       Balanced Accuracy : 0.6973        
##                                         
##        'Positive' Class : 1             
## 

library(xgboost)
moklas1.train.matrix<-data.matrix(moklas1.train[,-10])
promo<-as.matrix(as.factor(as.character(moklas1.train$promo)))
xgbModel <- xgboost(data = moklas1.train.matrix, 
                    label = promo,
                    nrounds = 1000,
                    max_depth = 2,
                    eta = 0.01,
                    objective = "binary:logistic")

## [1]  train-logloss:0.691055 
## [2]  train-logloss:0.689002 
## [3]  train-logloss:0.686987 
## [4]  train-logloss:0.685008 
## [5]  train-logloss:0.683067 
## [6]  train-logloss:0.681160 
## [7]  train-logloss:0.679289 
## [8]  train-logloss:0.677450 
## [9]  train-logloss:0.675646 
## [10] train-logloss:0.673873 
## [11] train-logloss:0.672133 
## [12] train-logloss:0.670423 
## [13] train-logloss:0.668744 
## [14] train-logloss:0.667094 
## [15] train-logloss:0.665474 
## [16] train-logloss:0.663882 
## [17] train-logloss:0.662318 
## [18] train-logloss:0.660782 
## [19] train-logloss:0.659272 
## [20] train-logloss:0.657789 
## [21] train-logloss:0.656331 
## [22] train-logloss:0.654899 
## [23] train-logloss:0.653491 
## [24] train-logloss:0.652108 
## [25] train-logloss:0.650748 
## [26] train-logloss:0.649411 
## [27] train-logloss:0.648098 
## [28] train-logloss:0.646807 
## [29] train-logloss:0.645537 
## [30] train-logloss:0.644209 
## [31] train-logloss:0.642977 
## [32] train-logloss:0.641688 
## [33] train-logloss:0.640423 
## [34] train-logloss:0.639242 
## [35] train-logloss:0.638014 
## [36] train-logloss:0.636867 
## [37] train-logloss:0.635673 
## [38] train-logloss:0.633674 
## [39] train-logloss:0.632514 
## [40] train-logloss:0.630563 
## [41] train-logloss:0.628648 
## [42] train-logloss:0.627591 
## [43] train-logloss:0.626487 
## [44] train-logloss:0.624631 
## [45] train-logloss:0.622809 
## [46] train-logloss:0.621746 
## [47] train-logloss:0.619966 
## [48] train-logloss:0.618218 
## [49] train-logloss:0.617257 
## [50] train-logloss:0.616244 
## [51] train-logloss:0.614549 
## [52] train-logloss:0.612885 
## [53] train-logloss:0.611908 
## [54] train-logloss:0.610282 
## [55] train-logloss:0.608685 
## [56] train-logloss:0.607742 
## [57] train-logloss:0.606182 
## [58] train-logloss:0.604650 
## [59] train-logloss:0.603805 
## [60] train-logloss:0.602903 
## [61] train-logloss:0.601417 
## [62] train-logloss:0.599957 
## [63] train-logloss:0.599086 
## [64] train-logloss:0.597659 
## [65] train-logloss:0.596257 
## [66] train-logloss:0.595473 
## [67] train-logloss:0.594102 
## [68] train-logloss:0.593278 
## [69] train-logloss:0.591938 
## [70] train-logloss:0.590621 
## [71] train-logloss:0.589824 
## [72] train-logloss:0.588536 
## [73] train-logloss:0.587271 
## [74] train-logloss:0.586498 
## [75] train-logloss:0.585260 
## [76] train-logloss:0.584044 
## [77] train-logloss:0.583295 
## [78] train-logloss:0.582106 
## [79] train-logloss:0.580938 
## [80] train-logloss:0.580258 
## [81] train-logloss:0.579114 
## [82] train-logloss:0.578400 
## [83] train-logloss:0.577282 
## [84] train-logloss:0.576182 
## [85] train-logloss:0.575489 
## [86] train-logloss:0.574414 
## [87] train-logloss:0.573355 
## [88] train-logloss:0.572727 
## [89] train-logloss:0.571689 
## [90] train-logloss:0.571028 
## [91] train-logloss:0.570012 
## [92] train-logloss:0.569409 
## [93] train-logloss:0.568412 
## [94] train-logloss:0.567775 
## [95] train-logloss:0.566799 
## [96] train-logloss:0.566194 
## [97] train-logloss:0.565619 
## [98] train-logloss:0.564666 
## [99] train-logloss:0.564080 
## [100]    train-logloss:0.563149 
## [101]    train-logloss:0.562547 
## [102]    train-logloss:0.561633 
## [103]    train-logloss:0.561069 
## [104]    train-logloss:0.560175 
## [105]    train-logloss:0.559623 
## [106]    train-logloss:0.559087 
## [107]    train-logloss:0.558212 
## [108]    train-logloss:0.557676 
## [109]    train-logloss:0.556821 
## [110]    train-logloss:0.556300 
## [111]    train-logloss:0.555776 
## [112]    train-logloss:0.554939 
## [113]    train-logloss:0.554426 
## [114]    train-logloss:0.553605 
## [115]    train-logloss:0.553101 
## [116]    train-logloss:0.552296 
## [117]    train-logloss:0.551801 
## [118]    train-logloss:0.551311 
## [119]    train-logloss:0.550523 
## [120]    train-logloss:0.550043 
## [121]    train-logloss:0.549270 
## [122]    train-logloss:0.548798 
## [123]    train-logloss:0.548324 
## [124]    train-logloss:0.547569 
## [125]    train-logloss:0.547108 
## [126]    train-logloss:0.546367 
## [127]    train-logloss:0.545912 
## [128]    train-logloss:0.545455 
## [129]    train-logloss:0.544728 
## [130]    train-logloss:0.544285 
## [131]    train-logloss:0.543839 
## [132]    train-logloss:0.543127 
## [133]    train-logloss:0.542694 
## [134]    train-logloss:0.541995 
## [135]    train-logloss:0.541568 
## [136]    train-logloss:0.541137 
## [137]    train-logloss:0.540660 
## [138]    train-logloss:0.540243 
## [139]    train-logloss:0.539775 
## [140]    train-logloss:0.539355 
## [141]    train-logloss:0.538894 
## [142]    train-logloss:0.538487 
## [143]    train-logloss:0.538077 
## [144]    train-logloss:0.537625 
## [145]    train-logloss:0.537167 
## [146]    train-logloss:0.536770 
## [147]    train-logloss:0.536326 
## [148]    train-logloss:0.535889 
## [149]    train-logloss:0.535442 
## [150]    train-logloss:0.535053 
## [151]    train-logloss:0.534664 
## [152]    train-logloss:0.534235 
## [153]    train-logloss:0.533855 
## [154]    train-logloss:0.533422 
## [155]    train-logloss:0.533001 
## [156]    train-logloss:0.532627 
## [157]    train-logloss:0.532213 
## [158]    train-logloss:0.531839 
## [159]    train-logloss:0.531431 
## [160]    train-logloss:0.531064 
## [161]    train-logloss:0.530646 
## [162]    train-logloss:0.530245 
## [163]    train-logloss:0.529885 
## [164]    train-logloss:0.529475 
## [165]    train-logloss:0.529081 
## [166]    train-logloss:0.528725 
## [167]    train-logloss:0.528336 
## [168]    train-logloss:0.527983 
## [169]    train-logloss:0.527586 
## [170]    train-logloss:0.527204 
## [171]    train-logloss:0.526857 
## [172]    train-logloss:0.526514 
## [173]    train-logloss:0.526137 
## [174]    train-logloss:0.525797 
## [175]    train-logloss:0.525426 
## [176]    train-logloss:0.525040 
## [177]    train-logloss:0.524705 
## [178]    train-logloss:0.524340 
## [179]    train-logloss:0.523963 
## [180]    train-logloss:0.523603 
## [181]    train-logloss:0.523274 
## [182]    train-logloss:0.522946 
## [183]    train-logloss:0.522591 
## [184]    train-logloss:0.522267 
## [185]    train-logloss:0.521898 
## [186]    train-logloss:0.521549 
## [187]    train-logloss:0.521229 
## [188]    train-logloss:0.520913 
## [189]    train-logloss:0.520342 
## [190]    train-logloss:0.519982 
## [191]    train-logloss:0.519669 
## [192]    train-logloss:0.519327 
## [193]    train-logloss:0.518991 
## [194]    train-logloss:0.518681 
## [195]    train-logloss:0.518375 
## [196]    train-logloss:0.518043 
## [197]    train-logloss:0.517693 
## [198]    train-logloss:0.517391 
## [199]    train-logloss:0.517064 
## [200]    train-logloss:0.516763 
## [201]    train-logloss:0.516467 
## [202]    train-logloss:0.515921 
## [203]    train-logloss:0.515580 
## [204]    train-logloss:0.515260 
## [205]    train-logloss:0.514968 
## [206]    train-logloss:0.514652 
## [207]    train-logloss:0.514360 
## [208]    train-logloss:0.514073 
## [209]    train-logloss:0.513741 
## [210]    train-logloss:0.513429 
## [211]    train-logloss:0.513146 
## [212]    train-logloss:0.512839 
## [213]    train-logloss:0.512555 
## [214]    train-logloss:0.512276 
## [215]    train-logloss:0.511754 
## [216]    train-logloss:0.511475 
## [217]    train-logloss:0.511174 
## [218]    train-logloss:0.510899 
## [219]    train-logloss:0.510579 
## [220]    train-logloss:0.510281 
## [221]    train-logloss:0.510011 
## [222]    train-logloss:0.509696 
## [223]    train-logloss:0.509402 
## [224]    train-logloss:0.509135 
## [225]    train-logloss:0.508846 
## [226]    train-logloss:0.508577 
## [227]    train-logloss:0.508314 
## [228]    train-logloss:0.507814 
## [229]    train-logloss:0.507506 
## [230]    train-logloss:0.507223 
## [231]    train-logloss:0.506962 
## [232]    train-logloss:0.506702 
## [233]    train-logloss:0.506212 
## [234]    train-logloss:0.505934 
## [235]    train-logloss:0.505679 
## [236]    train-logloss:0.505377 
## [237]    train-logloss:0.505103 
## [238]    train-logloss:0.504849 
## [239]    train-logloss:0.504598 
## [240]    train-logloss:0.504327 
## [241]    train-logloss:0.504060 
## [242]    train-logloss:0.503764 
## [243]    train-logloss:0.503516 
## [244]    train-logloss:0.503268 
## [245]    train-logloss:0.503004 
## [246]    train-logloss:0.502760 
## [247]    train-logloss:0.502473 
## [248]    train-logloss:0.502211 
## [249]    train-logloss:0.501970 
## [250]    train-logloss:0.501712 
## [251]    train-logloss:0.501469 
## [252]    train-logloss:0.501231 
## [253]    train-logloss:0.500976 
## [254]    train-logloss:0.500693 
## [255]    train-logloss:0.500458 
## [256]    train-logloss:0.499997 
## [257]    train-logloss:0.499760 
## [258]    train-logloss:0.499509 
## [259]    train-logloss:0.499278 
## [260]    train-logloss:0.499031 
## [261]    train-logloss:0.498797 
## [262]    train-logloss:0.498568 
## [263]    train-logloss:0.498293 
## [264]    train-logloss:0.498048 
## [265]    train-logloss:0.497822 
## [266]    train-logloss:0.497580 
## [267]    train-logloss:0.497350 
## [268]    train-logloss:0.496849 
## [269]    train-logloss:0.496627 
## [270]    train-logloss:0.496390 
## [271]    train-logloss:0.495900 
## [272]    train-logloss:0.495534 
## [273]    train-logloss:0.495314 
## [274]    train-logloss:0.495089 
## [275]    train-logloss:0.494729 
## [276]    train-logloss:0.494251 
## [277]    train-logloss:0.494036 
## [278]    train-logloss:0.493684 
## [279]    train-logloss:0.493457 
## [280]    train-logloss:0.493245 
## [281]    train-logloss:0.493023 
## [282]    train-logloss:0.492812 
## [283]    train-logloss:0.492588 
## [284]    train-logloss:0.492127 
## [285]    train-logloss:0.491783 
## [286]    train-logloss:0.491520 
## [287]    train-logloss:0.491182 
## [288]    train-logloss:0.490978 
## [289]    train-logloss:0.490757 
## [290]    train-logloss:0.490541 
## [291]    train-logloss:0.490090 
## [292]    train-logloss:0.489759 
## [293]    train-logloss:0.489559 
## [294]    train-logloss:0.489301 
## [295]    train-logloss:0.488976 
## [296]    train-logloss:0.488760 
## [297]    train-logloss:0.488562 
## [298]    train-logloss:0.488243 
## [299]    train-logloss:0.488028 
## [300]    train-logloss:0.487817 
## [301]    train-logloss:0.487622 
## [302]    train-logloss:0.487187 
## [303]    train-logloss:0.486874 
## [304]    train-logloss:0.486682 
## [305]    train-logloss:0.486471 
## [306]    train-logloss:0.486220 
## [307]    train-logloss:0.485911 
## [308]    train-logloss:0.485703 
## [309]    train-logloss:0.485280 
## [310]    train-logloss:0.485090 
## [311]    train-logloss:0.484884 
## [312]    train-logloss:0.484581 
## [313]    train-logloss:0.484395 
## [314]    train-logloss:0.484190 
## [315]    train-logloss:0.483776 
## [316]    train-logloss:0.483574 
## [317]    train-logloss:0.483389 
## [318]    train-logloss:0.482982 
## [319]    train-logloss:0.482686 
## [320]    train-logloss:0.482503 
## [321]    train-logloss:0.482212 
## [322]    train-logloss:0.482014 
## [323]    train-logloss:0.481813 
## [324]    train-logloss:0.481569 
## [325]    train-logloss:0.481370 
## [326]    train-logloss:0.480973 
## [327]    train-logloss:0.480686 
## [328]    train-logloss:0.480507 
## [329]    train-logloss:0.480331 
## [330]    train-logloss:0.479940 
## [331]    train-logloss:0.479658 
## [332]    train-logloss:0.479465 
## [333]    train-logloss:0.479270 
## [334]    train-logloss:0.479096 
## [335]    train-logloss:0.478722 
## [336]    train-logloss:0.478446 
## [337]    train-logloss:0.478078 
## [338]    train-logloss:0.477887 
## [339]    train-logloss:0.477717 
## [340]    train-logloss:0.477523 
## [341]    train-logloss:0.477285 
## [342]    train-logloss:0.476922 
## [343]    train-logloss:0.476651 
## [344]    train-logloss:0.476294 
## [345]    train-logloss:0.476124 
## [346]    train-logloss:0.475859 
## [347]    train-logloss:0.475508 
## [348]    train-logloss:0.475323 
## [349]    train-logloss:0.475156 
## [350]    train-logloss:0.474810 
## [351]    train-logloss:0.474618 
## [352]    train-logloss:0.474436 
## [353]    train-logloss:0.474271 
## [354]    train-logloss:0.474082 
## [355]    train-logloss:0.473740 
## [356]    train-logloss:0.473481 
## [357]    train-logloss:0.473146 
## [358]    train-logloss:0.472982 
## [359]    train-logloss:0.472803 
## [360]    train-logloss:0.472549 
## [361]    train-logloss:0.472218 
## [362]    train-logloss:0.472058 
## [363]    train-logloss:0.471732 
## [364]    train-logloss:0.471556 
## [365]    train-logloss:0.471397 
## [366]    train-logloss:0.471209 
## [367]    train-logloss:0.470976 
## [368]    train-logloss:0.470727 
## [369]    train-logloss:0.470542 
## [370]    train-logloss:0.470295 
## [371]    train-logloss:0.469974 
## [372]    train-logloss:0.469817 
## [373]    train-logloss:0.469645 
## [374]    train-logloss:0.469328 
## [375]    train-logloss:0.469173 
## [376]    train-logloss:0.468989 
## [377]    train-logloss:0.468761 
## [378]    train-logloss:0.468520 
## [379]    train-logloss:0.468339 
## [380]    train-logloss:0.468025 
## [381]    train-logloss:0.467787 
## [382]    train-logloss:0.467480 
## [383]    train-logloss:0.467327 
## [384]    train-logloss:0.467158 
## [385]    train-logloss:0.466978 
## [386]    train-logloss:0.466828 
## [387]    train-logloss:0.466651 
## [388]    train-logloss:0.466426 
## [389]    train-logloss:0.466192 
## [390]    train-logloss:0.465888 
## [391]    train-logloss:0.465753 
## [392]    train-logloss:0.465453 
## [393]    train-logloss:0.465222 
## [394]    train-logloss:0.465073 
## [395]    train-logloss:0.464898 
## [396]    train-logloss:0.464732 
## [397]    train-logloss:0.464598 
## [398]    train-logloss:0.464302 
## [399]    train-logloss:0.464155 
## [400]    train-logloss:0.463982 
## [401]    train-logloss:0.463818 
## [402]    train-logloss:0.463685 
## [403]    train-logloss:0.463394 
## [404]    train-logloss:0.463166 
## [405]    train-logloss:0.462995 
## [406]    train-logloss:0.462850 
## [407]    train-logloss:0.462629 
## [408]    train-logloss:0.462406 
## [409]    train-logloss:0.462237 
## [410]    train-logloss:0.461914 
## [411]    train-logloss:0.461783 
## [412]    train-logloss:0.461563 
## [413]    train-logloss:0.461420 
## [414]    train-logloss:0.461134 
## [415]    train-logloss:0.460973 
## [416]    train-logloss:0.460843 
## [417]    train-logloss:0.460676 
## [418]    train-logloss:0.460535 
## [419]    train-logloss:0.460253 
## [420]    train-logloss:0.460036 
## [421]    train-logloss:0.459871 
## [422]    train-logloss:0.459557 
## [423]    train-logloss:0.459398 
## [424]    train-logloss:0.459257 
## [425]    train-logloss:0.459129 
## [426]    train-logloss:0.458916 
## [427]    train-logloss:0.458753 
## [428]    train-logloss:0.458444 
## [429]    train-logloss:0.458306 
## [430]    train-logloss:0.458148 
## [431]    train-logloss:0.458022 
## [432]    train-logloss:0.457812 
## [433]    train-logloss:0.457537 
## [434]    train-logloss:0.457400 
## [435]    train-logloss:0.457239 
## [436]    train-logloss:0.457083 
## [437]    train-logloss:0.456958 
## [438]    train-logloss:0.456823 
## [439]    train-logloss:0.456552 
## [440]    train-logloss:0.456344 
## [441]    train-logloss:0.456184 
## [442]    train-logloss:0.455885 
## [443]    train-logloss:0.455730 
## [444]    train-logloss:0.455596 
## [445]    train-logloss:0.455473 
## [446]    train-logloss:0.455269 
## [447]    train-logloss:0.455111 
## [448]    train-logloss:0.454816 
## [449]    train-logloss:0.454615 
## [450]    train-logloss:0.454325 
## [451]    train-logloss:0.454193 
## [452]    train-logloss:0.454040 
## [453]    train-logloss:0.453917 
## [454]    train-logloss:0.453762 
## [455]    train-logloss:0.453476 
## [456]    train-logloss:0.453278 
## [457]    train-logloss:0.453146 
## [458]    train-logloss:0.453026 
## [459]    train-logloss:0.452874 
## [460]    train-logloss:0.452745 
## [461]    train-logloss:0.452626 
## [462]    train-logloss:0.452471 
## [463]    train-logloss:0.452189 
## [464]    train-logloss:0.451994 
## [465]    train-logloss:0.451842 
## [466]    train-logloss:0.451692 
## [467]    train-logloss:0.451564 
## [468]    train-logloss:0.451287 
## [469]    train-logloss:0.451094 
## [470]    train-logloss:0.450977 
## [471]    train-logloss:0.450849 
## [472]    train-logloss:0.450733 
## [473]    train-logloss:0.450476 
## [474]    train-logloss:0.450328 
## [475]    train-logloss:0.450177 
## [476]    train-logloss:0.449987 
## [477]    train-logloss:0.449775 
## [478]    train-logloss:0.449626 
## [479]    train-logloss:0.449355 
## [480]    train-logloss:0.449168 
## [481]    train-logloss:0.449043 
## [482]    train-logloss:0.448776 
## [483]    train-logloss:0.448651 
## [484]    train-logloss:0.448536 
## [485]    train-logloss:0.448352 
## [486]    train-logloss:0.448239 
## [487]    train-logloss:0.448091 
## [488]    train-logloss:0.447944 
## [489]    train-logloss:0.447693 
## [490]    train-logloss:0.447511 
## [491]    train-logloss:0.447388 
## [492]    train-logloss:0.447242 
## [493]    train-logloss:0.446981 
## [494]    train-logloss:0.446836 
## [495]    train-logloss:0.446714 
## [496]    train-logloss:0.446602 
## [497]    train-logloss:0.446422 
## [498]    train-logloss:0.446312 
## [499]    train-logloss:0.446066 
## [500]    train-logloss:0.445888 
## [501]    train-logloss:0.445744 
## [502]    train-logloss:0.445623 
## [503]    train-logloss:0.445479 
## [504]    train-logloss:0.445336 
## [505]    train-logloss:0.445080 
## [506]    train-logloss:0.444905 
## [507]    train-logloss:0.444785 
## [508]    train-logloss:0.444643 
## [509]    train-logloss:0.444533 
## [510]    train-logloss:0.444281 
## [511]    train-logloss:0.444162 
## [512]    train-logloss:0.444021 
## [513]    train-logloss:0.443912 
## [514]    train-logloss:0.443771 
## [515]    train-logloss:0.443522 
## [516]    train-logloss:0.443349 
## [517]    train-logloss:0.443231 
## [518]    train-logloss:0.443091 
## [519]    train-logloss:0.442984 
## [520]    train-logloss:0.442844 
## [521]    train-logloss:0.442598 
## [522]    train-logloss:0.442481 
## [523]    train-logloss:0.442310 
## [524]    train-logloss:0.442205 
## [525]    train-logloss:0.441967 
## [526]    train-logloss:0.441828 
## [527]    train-logloss:0.441711 
## [528]    train-logloss:0.441575 
## [529]    train-logloss:0.441340 
## [530]    train-logloss:0.441171 
## [531]    train-logloss:0.441032 
## [532]    train-logloss:0.440927 
## [533]    train-logloss:0.440722 
## [534]    train-logloss:0.440555 
## [535]    train-logloss:0.440418 
## [536]    train-logloss:0.440179 
## [537]    train-logloss:0.440062 
## [538]    train-logloss:0.439898 
## [539]    train-logloss:0.439795 
## [540]    train-logloss:0.439658 
## [541]    train-logloss:0.439522 
## [542]    train-logloss:0.439286 
## [543]    train-logloss:0.439171 
## [544]    train-logloss:0.439008 
## [545]    train-logloss:0.438776 
## [546]    train-logloss:0.438641 
## [547]    train-logloss:0.438526 
## [548]    train-logloss:0.438423 
## [549]    train-logloss:0.438263 
## [550]    train-logloss:0.438129 
## [551]    train-logloss:0.437899 
## [552]    train-logloss:0.437765 
## [553]    train-logloss:0.437651 
## [554]    train-logloss:0.437550 
## [555]    train-logloss:0.437451 
## [556]    train-logloss:0.437292 
## [557]    train-logloss:0.437159 
## [558]    train-logloss:0.437027 
## [559]    train-logloss:0.436798 
## [560]    train-logloss:0.436685 
## [561]    train-logloss:0.436554 
## [562]    train-logloss:0.436455 
## [563]    train-logloss:0.436230 
## [564]    train-logloss:0.436073 
## [565]    train-logloss:0.435941 
## [566]    train-logloss:0.435810 
## [567]    train-logloss:0.435698 
## [568]    train-logloss:0.435498 
## [569]    train-logloss:0.435343 
## [570]    train-logloss:0.435120 
## [571]    train-logloss:0.434966 
## [572]    train-logloss:0.434835 
## [573]    train-logloss:0.434738 
## [574]    train-logloss:0.434518 
## [575]    train-logloss:0.434367 
## [576]    train-logloss:0.434237 
## [577]    train-logloss:0.434125 
## [578]    train-logloss:0.433996 
## [579]    train-logloss:0.433774 
## [580]    train-logloss:0.433624 
## [581]    train-logloss:0.433496 
## [582]    train-logloss:0.433385 
## [583]    train-logloss:0.433289 
## [584]    train-logloss:0.433072 
## [585]    train-logloss:0.432945 
## [586]    train-logloss:0.432796 
## [587]    train-logloss:0.432668 
## [588]    train-logloss:0.432450 
## [589]    train-logloss:0.432340 
## [590]    train-logloss:0.432214 
## [591]    train-logloss:0.432119 
## [592]    train-logloss:0.432010 
## [593]    train-logloss:0.431814 
## [594]    train-logloss:0.431720 
## [595]    train-logloss:0.431507 
## [596]    train-logloss:0.431360 
## [597]    train-logloss:0.431232 
## [598]    train-logloss:0.431107 
## [599]    train-logloss:0.430962 
## [600]    train-logloss:0.430836 
## [601]    train-logloss:0.430622 
## [602]    train-logloss:0.430514 
## [603]    train-logloss:0.430421 
## [604]    train-logloss:0.430297 
## [605]    train-logloss:0.430086 
## [606]    train-logloss:0.429943 
## [607]    train-logloss:0.429835 
## [608]    train-logloss:0.429710 
## [609]    train-logloss:0.429501 
## [610]    train-logloss:0.429378 
## [611]    train-logloss:0.429236 
## [612]    train-logloss:0.429128 
## [613]    train-logloss:0.429036 
## [614]    train-logloss:0.428914 
## [615]    train-logloss:0.428708 
## [616]    train-logloss:0.428567 
## [617]    train-logloss:0.428443 
## [618]    train-logloss:0.428250 
## [619]    train-logloss:0.428160 
## [620]    train-logloss:0.428021 
## [621]    train-logloss:0.427899 
## [622]    train-logloss:0.427694 
## [623]    train-logloss:0.427586 
## [624]    train-logloss:0.427464 
## [625]    train-logloss:0.427375 
## [626]    train-logloss:0.427238 
## [627]    train-logloss:0.427035 
## [628]    train-logloss:0.426928 
## [629]    train-logloss:0.426808 
## [630]    train-logloss:0.426720 
## [631]    train-logloss:0.426584 
## [632]    train-logloss:0.426462 
## [633]    train-logloss:0.426262 
## [634]    train-logloss:0.426156 
## [635]    train-logloss:0.426037 
## [636]    train-logloss:0.425950 
## [637]    train-logloss:0.425780 
## [638]    train-logloss:0.425659 
## [639]    train-logloss:0.425462 
## [640]    train-logloss:0.425356 
## [641]    train-logloss:0.425238 
## [642]    train-logloss:0.425104 
## [643]    train-logloss:0.424904 
## [644]    train-logloss:0.424786 
## [645]    train-logloss:0.424681 
## [646]    train-logloss:0.424548 
## [647]    train-logloss:0.424462 
## [648]    train-logloss:0.424295 
## [649]    train-logloss:0.424176 
## [650]    train-logloss:0.424059 
## [651]    train-logloss:0.423866 
## [652]    train-logloss:0.423761 
## [653]    train-logloss:0.423573 
## [654]    train-logloss:0.423442 
## [655]    train-logloss:0.423323 
## [656]    train-logloss:0.423239 
## [657]    train-logloss:0.423042 
## [658]    train-logloss:0.422912 
## [659]    train-logloss:0.422796 
## [660]    train-logloss:0.422692 
## [661]    train-logloss:0.422528 
## [662]    train-logloss:0.422445 
## [663]    train-logloss:0.422330 
## [664]    train-logloss:0.422135 
## [665]    train-logloss:0.422007 
## [666]    train-logloss:0.421889 
## [667]    train-logloss:0.421786 
## [668]    train-logloss:0.421671 
## [669]    train-logloss:0.421483 
## [670]    train-logloss:0.421380 
## [671]    train-logloss:0.421267 
## [672]    train-logloss:0.421140 
## [673]    train-logloss:0.421023 
## [674]    train-logloss:0.420837 
## [675]    train-logloss:0.420735 
## [676]    train-logloss:0.420622 
## [677]    train-logloss:0.420496 
## [678]    train-logloss:0.420306 
## [679]    train-logloss:0.420122 
## [680]    train-logloss:0.420040 
## [681]    train-logloss:0.419879 
## [682]    train-logloss:0.419778 
## [683]    train-logloss:0.419666 
## [684]    train-logloss:0.419549 
## [685]    train-logloss:0.419425 
## [686]    train-logloss:0.419310 
## [687]    train-logloss:0.419127 
## [688]    train-logloss:0.419027 
## [689]    train-logloss:0.418915 
## [690]    train-logloss:0.418834 
## [691]    train-logloss:0.418674 
## [692]    train-logloss:0.418551 
## [693]    train-logloss:0.418371 
## [694]    train-logloss:0.418261 
## [695]    train-logloss:0.418161 
## [696]    train-logloss:0.418081 
## [697]    train-logloss:0.417967 
## [698]    train-logloss:0.417857 
## [699]    train-logloss:0.417679 
## [700]    train-logloss:0.417557 
## [701]    train-logloss:0.417458 
## [702]    train-logloss:0.417300 
## [703]    train-logloss:0.417191 
## [704]    train-logloss:0.417112 
## [705]    train-logloss:0.416999 
## [706]    train-logloss:0.416824 
## [707]    train-logloss:0.416703 
## [708]    train-logloss:0.416604 
## [709]    train-logloss:0.416424 
## [710]    train-logloss:0.416312 
## [711]    train-logloss:0.416139 
## [712]    train-logloss:0.416019 
## [713]    train-logloss:0.415942 
## [714]    train-logloss:0.415833 
## [715]    train-logloss:0.415735 
## [716]    train-logloss:0.415580 
## [717]    train-logloss:0.415503 
## [718]    train-logloss:0.415351 
## [719]    train-logloss:0.415243 
## [720]    train-logloss:0.415167 
## [721]    train-logloss:0.415016 
## [722]    train-logloss:0.414904 
## [723]    train-logloss:0.414797 
## [724]    train-logloss:0.414626 
## [725]    train-logloss:0.414528 
## [726]    train-logloss:0.414410 
## [727]    train-logloss:0.414300 
## [728]    train-logloss:0.414131 
## [729]    train-logloss:0.414034 
## [730]    train-logloss:0.413927 
## [731]    train-logloss:0.413810 
## [732]    train-logloss:0.413736 
## [733]    train-logloss:0.413587 
## [734]    train-logloss:0.413481 
## [735]    train-logloss:0.413384 
## [736]    train-logloss:0.413203 
## [737]    train-logloss:0.413087 
## [738]    train-logloss:0.412977 
## [739]    train-logloss:0.412872 
## [740]    train-logloss:0.412707 
## [741]    train-logloss:0.412610 
## [742]    train-logloss:0.412537 
## [743]    train-logloss:0.412432 
## [744]    train-logloss:0.412317 
## [745]    train-logloss:0.412142 
## [746]    train-logloss:0.412032 
## [747]    train-logloss:0.411937 
## [748]    train-logloss:0.411774 
## [749]    train-logloss:0.411661 
## [750]    train-logloss:0.411557 
## [751]    train-logloss:0.411449 
## [752]    train-logloss:0.411287 
## [753]    train-logloss:0.411192 
## [754]    train-logloss:0.411080 
## [755]    train-logloss:0.411008 
## [756]    train-logloss:0.410860 
## [757]    train-logloss:0.410757 
## [758]    train-logloss:0.410686 
## [759]    train-logloss:0.410508 
## [760]    train-logloss:0.410406 
## [761]    train-logloss:0.410312 
## [762]    train-logloss:0.410200 
## [763]    train-logloss:0.410093 
## [764]    train-logloss:0.409991 
## [765]    train-logloss:0.409897 
## [766]    train-logloss:0.409721 
## [767]    train-logloss:0.409549 
## [768]    train-logloss:0.409438 
## [769]    train-logloss:0.409332 
## [770]    train-logloss:0.409187 
## [771]    train-logloss:0.409117 
## [772]    train-logloss:0.409015 
## [773]    train-logloss:0.408910 
## [774]    train-logloss:0.408752 
## [775]    train-logloss:0.408659 
## [776]    train-logloss:0.408550 
## [777]    train-logloss:0.408485 
## [778]    train-logloss:0.408385 
## [779]    train-logloss:0.408241 
## [780]    train-logloss:0.408086 
## [781]    train-logloss:0.407993 
## [782]    train-logloss:0.407893 
## [783]    train-logloss:0.407785 
## [784]    train-logloss:0.407680 
## [785]    train-logloss:0.407617 
## [786]    train-logloss:0.407475 
## [787]    train-logloss:0.407376 
## [788]    train-logloss:0.407221 
## [789]    train-logloss:0.407129 
## [790]    train-logloss:0.407025 
## [791]    train-logloss:0.406926 
## [792]    train-logloss:0.406834 
## [793]    train-logloss:0.406665 
## [794]    train-logloss:0.406559 
## [795]    train-logloss:0.406456 
## [796]    train-logloss:0.406303 
## [797]    train-logloss:0.406197 
## [798]    train-logloss:0.406135 
## [799]    train-logloss:0.405996 
## [800]    train-logloss:0.405897 
## [801]    train-logloss:0.405727 
## [802]    train-logloss:0.405636 
## [803]    train-logloss:0.405538 
## [804]    train-logloss:0.405476 
## [805]    train-logloss:0.405339 
## [806]    train-logloss:0.405236 
## [807]    train-logloss:0.405139 
## [808]    train-logloss:0.405034 
## [809]    train-logloss:0.404944 
## [810]    train-logloss:0.404775 
## [811]    train-logloss:0.404678 
## [812]    train-logloss:0.404574 
## [813]    train-logloss:0.404472 
## [814]    train-logloss:0.404323 
## [815]    train-logloss:0.404233 
## [816]    train-logloss:0.404137 
## [817]    train-logloss:0.404036 
## [818]    train-logloss:0.403889 
## [819]    train-logloss:0.403723 
## [820]    train-logloss:0.403620 
## [821]    train-logloss:0.403531 
## [822]    train-logloss:0.403435 
## [823]    train-logloss:0.403335 
## [824]    train-logloss:0.403275 
## [825]    train-logloss:0.403139 
## [826]    train-logloss:0.402993 
## [827]    train-logloss:0.402903 
## [828]    train-logloss:0.402808 
## [829]    train-logloss:0.402706 
## [830]    train-logloss:0.402542 
## [831]    train-logloss:0.402448 
## [832]    train-logloss:0.402346 
## [833]    train-logloss:0.402258 
## [834]    train-logloss:0.402159 
## [835]    train-logloss:0.402064 
## [836]    train-logloss:0.401966 
## [837]    train-logloss:0.401823 
## [838]    train-logloss:0.401735 
## [839]    train-logloss:0.401635 
## [840]    train-logloss:0.401541 
## [841]    train-logloss:0.401481 
## [842]    train-logloss:0.401338 
## [843]    train-logloss:0.401251 
## [844]    train-logloss:0.401158 
## [845]    train-logloss:0.400995 
## [846]    train-logloss:0.400896 
## [847]    train-logloss:0.400799 
## [848]    train-logloss:0.400702 
## [849]    train-logloss:0.400561 
## [850]    train-logloss:0.400475 
## [851]    train-logloss:0.400416 
## [852]    train-logloss:0.400323 
## [853]    train-logloss:0.400189 
## [854]    train-logloss:0.400131 
## [855]    train-logloss:0.400033 
## [856]    train-logloss:0.399901 
## [857]    train-logloss:0.399808 
## [858]    train-logloss:0.399648 
## [859]    train-logloss:0.399561 
## [860]    train-logloss:0.399470 
## [861]    train-logloss:0.399374 
## [862]    train-logloss:0.399277 
## [863]    train-logloss:0.399137 
## [864]    train-logloss:0.399042 
## [865]    train-logloss:0.398951 
## [866]    train-logloss:0.398865 
## [867]    train-logloss:0.398705 
## [868]    train-logloss:0.398609 
## [869]    train-logloss:0.398451 
## [870]    train-logloss:0.398355 
## [871]    train-logloss:0.398261 
## [872]    train-logloss:0.398170 
## [873]    train-logloss:0.398085 
## [874]    train-logloss:0.398028 
## [875]    train-logloss:0.397897 
## [876]    train-logloss:0.397807 
## [877]    train-logloss:0.397714 
## [878]    train-logloss:0.397577 
## [879]    train-logloss:0.397492 
## [880]    train-logloss:0.397403 
## [881]    train-logloss:0.397310 
## [882]    train-logloss:0.397174 
## [883]    train-logloss:0.397079 
## [884]    train-logloss:0.396995 
## [885]    train-logloss:0.396906 
## [886]    train-logloss:0.396751 
## [887]    train-logloss:0.396657 
## [888]    train-logloss:0.396569 
## [889]    train-logloss:0.396485 
## [890]    train-logloss:0.396429 
## [891]    train-logloss:0.396300 
## [892]    train-logloss:0.396208 
## [893]    train-logloss:0.396050 
## [894]    train-logloss:0.395996 
## [895]    train-logloss:0.395868 
## [896]    train-logloss:0.395780 
## [897]    train-logloss:0.395688 
## [898]    train-logloss:0.395555 
## [899]    train-logloss:0.395462 
## [900]    train-logloss:0.395378 
## [901]    train-logloss:0.395291 
## [902]    train-logloss:0.395138 
## [903]    train-logloss:0.395046 
## [904]    train-logloss:0.394963 
## [905]    train-logloss:0.394876 
## [906]    train-logloss:0.394785 
## [907]    train-logloss:0.394654 
## [908]    train-logloss:0.394567 
## [909]    train-logloss:0.394437 
## [910]    train-logloss:0.394354 
## [911]    train-logloss:0.394263 
## [912]    train-logloss:0.394176 
## [913]    train-logloss:0.394086 
## [914]    train-logloss:0.394004 
## [915]    train-logloss:0.393850 
## [916]    train-logloss:0.393795 
## [917]    train-logloss:0.393705 
## [918]    train-logloss:0.393616 
## [919]    train-logloss:0.393487 
## [920]    train-logloss:0.393398 
## [921]    train-logloss:0.393312 
## [922]    train-logloss:0.393184 
## [923]    train-logloss:0.393102 
## [924]    train-logloss:0.393013 
## [925]    train-logloss:0.392959 
## [926]    train-logloss:0.392833 
## [927]    train-logloss:0.392748 
## [928]    train-logloss:0.392667 
## [929]    train-logloss:0.392582 
## [930]    train-logloss:0.392433 
## [931]    train-logloss:0.392344 
## [932]    train-logloss:0.392257 
## [933]    train-logloss:0.392172 
## [934]    train-logloss:0.392091 
## [935]    train-logloss:0.392038 
## [936]    train-logloss:0.391892 
## [937]    train-logloss:0.391804 
## [938]    train-logloss:0.391719 
## [939]    train-logloss:0.391640 
## [940]    train-logloss:0.391488 
## [941]    train-logloss:0.391400 
## [942]    train-logloss:0.391276 
## [943]    train-logloss:0.391192 
## [944]    train-logloss:0.391106 
## [945]    train-logloss:0.390980 
## [946]    train-logloss:0.390893 
## [947]    train-logloss:0.390810 
## [948]    train-logloss:0.390665 
## [949]    train-logloss:0.390586 
## [950]    train-logloss:0.390499 
## [951]    train-logloss:0.390413 
## [952]    train-logloss:0.390330 
## [953]    train-logloss:0.390244 
## [954]    train-logloss:0.390165 
## [955]    train-logloss:0.390016 
## [956]    train-logloss:0.389872 
## [957]    train-logloss:0.389787 
## [958]    train-logloss:0.389735 
## [959]    train-logloss:0.389611 
## [960]    train-logloss:0.389527 
## [961]    train-logloss:0.389444 
## [962]    train-logloss:0.389365 
## [963]    train-logloss:0.389243 
## [964]    train-logloss:0.389192 
## [965]    train-logloss:0.389110 
## [966]    train-logloss:0.389025 
## [967]    train-logloss:0.388883 
## [968]    train-logloss:0.388799 
## [969]    train-logloss:0.388717 
## [970]    train-logloss:0.388577 
## [971]    train-logloss:0.388499 
## [972]    train-logloss:0.388448 
## [973]    train-logloss:0.388327 
## [974]    train-logloss:0.388246 
## [975]    train-logloss:0.388162 
## [976]    train-logloss:0.388022 
## [977]    train-logloss:0.387939 
## [978]    train-logloss:0.387858 
## [979]    train-logloss:0.387780 
## [980]    train-logloss:0.387697 
## [981]    train-logloss:0.387576 
## [982]    train-logloss:0.387498 
## [983]    train-logloss:0.387418 
## [984]    train-logloss:0.387368 
## [985]    train-logloss:0.387248 
## [986]    train-logloss:0.387168 
## [987]    train-logloss:0.387085 
## [988]    train-logloss:0.386965 
## [989]    train-logloss:0.386916 
## [990]    train-logloss:0.386778 
## [991]    train-logloss:0.386696 
## [992]    train-logloss:0.386615 
## [993]    train-logloss:0.386533 
## [994]    train-logloss:0.386414 
## [995]    train-logloss:0.386337 
## [996]    train-logloss:0.386257 
## [997]    train-logloss:0.386181 
## [998]    train-logloss:0.386062 
## [999]    train-logloss:0.385981 
## [1000]   train-logloss:0.385902

## Plot Train error.

plot(xgbModel$evaluation_log, type = "l")

## Plot feature importance
importance <- xgb.importance(model = xgbModel)

xgb.plot.importance(importance)

## Make predictions on test data 
moklas1.test.matrix<-data.matrix(moklas1.test[,-10])
promo.test<-as.matrix(as.factor(as.character(moklas1.test$promo)))
predicted <- predict(xgbModel,moklas1.test.matrix )

predicted <- ifelse(predicted > 0.5 , 1,0)

## Create confusion matrix

confusionMatrix(table(predicted = predicted, actual = promo.test))

## Confusion Matrix and Statistics
## 
##          actual
## predicted  0  1
##         0 43 11
##         1  4 13
##                                           
##                Accuracy : 0.7887          
##                  95% CI : (0.6756, 0.8767)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.01404         
##                                           
##                   Kappa : 0.4916          
##                                           
##  Mcnemar's Test P-Value : 0.12134         
##                                           
##             Sensitivity : 0.9149          
##             Specificity : 0.5417          
##          Pos Pred Value : 0.7963          
##          Neg Pred Value : 0.7647          
##              Prevalence : 0.6620          
##          Detection Rate : 0.6056          
##    Detection Prevalence : 0.7606          
##       Balanced Accuracy : 0.7283          
##                                           
##        'Positive' Class : 0               
## 

RUS

library(xgboost)
down.train.matrix<-data.matrix(down_train[,-10])
promo1<-as.matrix(as.factor(as.character(down_train$promo)))
xgbModel1 <- xgboost(data = down.train.matrix, 
                    label = promo1,
                    nrounds = 1000,
                    max_depth = 2,
                    eta = 0.01,
                    objective = "binary:logistic")

## [1]  train-logloss:0.691789 
## [2]  train-logloss:0.690704 
## [3]  train-logloss:0.689380 
## [4]  train-logloss:0.688323 
## [5]  train-logloss:0.687283 
## [6]  train-logloss:0.686000 
## [7]  train-logloss:0.684986 
## [8]  train-logloss:0.683734 
## [9]  train-logloss:0.682745 
## [10] train-logloss:0.681524 
## [11] train-logloss:0.680550 
## [12] train-logloss:0.679357 
## [13] train-logloss:0.678185 
## [14] train-logloss:0.677243 
## [15] train-logloss:0.676097 
## [16] train-logloss:0.675169 
## [17] train-logloss:0.674051 
## [18] train-logloss:0.673142 
## [19] train-logloss:0.672049 
## [20] train-logloss:0.670975 
## [21] train-logloss:0.670089 
## [22] train-logloss:0.669040 
## [23] train-logloss:0.668170 
## [24] train-logloss:0.667144 
## [25] train-logloss:0.666291 
## [26] train-logloss:0.665290 
## [27] train-logloss:0.664450 
## [28] train-logloss:0.663471 
## [29] train-logloss:0.662648 
## [30] train-logloss:0.661692 
## [31] train-logloss:0.660881 
## [32] train-logloss:0.659945 
## [33] train-logloss:0.659024 
## [34] train-logloss:0.658236 
## [35] train-logloss:0.657336 
## [36] train-logloss:0.656558 
## [37] train-logloss:0.655679 
## [38] train-logloss:0.654917 
## [39] train-logloss:0.654056 
## [40] train-logloss:0.652603 
## [41] train-logloss:0.651858 
## [42] train-logloss:0.650437 
## [43] train-logloss:0.649603 
## [44] train-logloss:0.648211 
## [45] train-logloss:0.646844 
## [46] train-logloss:0.646130 
## [47] train-logloss:0.644793 
## [48] train-logloss:0.643479 
## [49] train-logloss:0.642786 
## [50] train-logloss:0.641498 
## [51] train-logloss:0.640231 
## [52] train-logloss:0.639441 
## [53] train-logloss:0.638199 
## [54] train-logloss:0.637533 
## [55] train-logloss:0.636314 
## [56] train-logloss:0.635660 
## [57] train-logloss:0.634464 
## [58] train-logloss:0.633703 
## [59] train-logloss:0.633063 
## [60] train-logloss:0.631890 
## [61] train-logloss:0.630737 
## [62] train-logloss:0.630110 
## [63] train-logloss:0.629490 
## [64] train-logloss:0.628363 
## [65] train-logloss:0.627752 
## [66] train-logloss:0.627023 
## [67] train-logloss:0.625918 
## [68] train-logloss:0.625319 
## [69] train-logloss:0.624235 
## [70] train-logloss:0.623169 
## [71] train-logloss:0.622584 
## [72] train-logloss:0.621880 
## [73] train-logloss:0.620833 
## [74] train-logloss:0.620259 
## [75] train-logloss:0.619232 
## [76] train-logloss:0.618668 
## [77] train-logloss:0.617986 
## [78] train-logloss:0.616979 
## [79] train-logloss:0.616426 
## [80] train-logloss:0.615437 
## [81] train-logloss:0.614465 
## [82] train-logloss:0.613925 
## [83] train-logloss:0.613389 
## [84] train-logloss:0.612436 
## [85] train-logloss:0.611911 
## [86] train-logloss:0.610975 
## [87] train-logloss:0.610055 
## [88] train-logloss:0.609538 
## [89] train-logloss:0.608890 
## [90] train-logloss:0.607985 
## [91] train-logloss:0.607476 
## [92] train-logloss:0.606586 
## [93] train-logloss:0.606084 
## [94] train-logloss:0.605451 
## [95] train-logloss:0.604576 
## [96] train-logloss:0.604083 
## [97] train-logloss:0.603222 
## [98] train-logloss:0.602376 
## [99] train-logloss:0.601889 
## [100]    train-logloss:0.601270 
## [101]    train-logloss:0.600436 
## [102]    train-logloss:0.599616 
## [103]    train-logloss:0.599139 
## [104]    train-logloss:0.598330 
## [105]    train-logloss:0.597860 
## [106]    train-logloss:0.597256 
## [107]    train-logloss:0.596460 
## [108]    train-logloss:0.595677 
## [109]    train-logloss:0.595216 
## [110]    train-logloss:0.594445 
## [111]    train-logloss:0.593991 
## [112]    train-logloss:0.593469 
## [113]    train-logloss:0.592708 
## [114]    train-logloss:0.592195 
## [115]    train-logloss:0.591447 
## [116]    train-logloss:0.591002 
## [117]    train-logloss:0.590265 
## [118]    train-logloss:0.589761 
## [119]    train-logloss:0.589266 
## [120]    train-logloss:0.588539 
## [121]    train-logloss:0.588052 
## [122]    train-logloss:0.587336 
## [123]    train-logloss:0.586902 
## [124]    train-logloss:0.586425 
## [125]    train-logloss:0.585718 
## [126]    train-logloss:0.585248 
## [127]    train-logloss:0.584552 
## [128]    train-logloss:0.584089 
## [129]    train-logloss:0.583404 
## [130]    train-logloss:0.582949 
## [131]    train-logloss:0.582272 
## [132]    train-logloss:0.581849 
## [133]    train-logloss:0.581403 
## [134]    train-logloss:0.580736 
## [135]    train-logloss:0.580297 
## [136]    train-logloss:0.579640 
## [137]    train-logloss:0.579207 
## [138]    train-logloss:0.578560 
## [139]    train-logloss:0.578145 
## [140]    train-logloss:0.577719 
## [141]    train-logloss:0.577081 
## [142]    train-logloss:0.576672 
## [143]    train-logloss:0.576253 
## [144]    train-logloss:0.575622 
## [145]    train-logloss:0.575207 
## [146]    train-logloss:0.574586 
## [147]    train-logloss:0.574184 
## [148]    train-logloss:0.573774 
## [149]    train-logloss:0.573159 
## [150]    train-logloss:0.572763 
## [151]    train-logloss:0.572359 
## [152]    train-logloss:0.571752 
## [153]    train-logloss:0.571361 
## [154]    train-logloss:0.570961 
## [155]    train-logloss:0.570362 
## [156]    train-logloss:0.569967 
## [157]    train-logloss:0.569583 
## [158]    train-logloss:0.568990 
## [159]    train-logloss:0.568600 
## [160]    train-logloss:0.568221 
## [161]    train-logloss:0.567636 
## [162]    train-logloss:0.567250 
## [163]    train-logloss:0.566673 
## [164]    train-logloss:0.566300 
## [165]    train-logloss:0.565918 
## [166]    train-logloss:0.565550 
## [167]    train-logloss:0.564978 
## [168]    train-logloss:0.564602 
## [169]    train-logloss:0.564039 
## [170]    train-logloss:0.563675 
## [171]    train-logloss:0.563304 
## [172]    train-logloss:0.562936 
## [173]    train-logloss:0.562379 
## [174]    train-logloss:0.562021 
## [175]    train-logloss:0.561470 
## [176]    train-logloss:0.561107 
## [177]    train-logloss:0.560754 
## [178]    train-logloss:0.560396 
## [179]    train-logloss:0.559852 
## [180]    train-logloss:0.559504 
## [181]    train-logloss:0.559150 
## [182]    train-logloss:0.558612 
## [183]    train-logloss:0.558269 
## [184]    train-logloss:0.557919 
## [185]    train-logloss:0.557387 
## [186]    train-logloss:0.557049 
## [187]    train-logloss:0.556704 
## [188]    train-logloss:0.556179 
## [189]    train-logloss:0.555844 
## [190]    train-logloss:0.555503 
## [191]    train-logloss:0.554984 
## [192]    train-logloss:0.554646 
## [193]    train-logloss:0.554134 
## [194]    train-logloss:0.553804 
## [195]    train-logloss:0.553471 
## [196]    train-logloss:0.553147 
## [197]    train-logloss:0.552639 
## [198]    train-logloss:0.552310 
## [199]    train-logloss:0.551809 
## [200]    train-logloss:0.551483 
## [201]    train-logloss:0.551163 
## [202]    train-logloss:0.550668 
## [203]    train-logloss:0.550352 
## [204]    train-logloss:0.550030 
## [205]    train-logloss:0.549541 
## [206]    train-logloss:0.549229 
## [207]    train-logloss:0.548911 
## [208]    train-logloss:0.548428 
## [209]    train-logloss:0.548112 
## [210]    train-logloss:0.547805 
## [211]    train-logloss:0.547327 
## [212]    train-logloss:0.547016 
## [213]    train-logloss:0.546713 
## [214]    train-logloss:0.546406 
## [215]    train-logloss:0.545935 
## [216]    train-logloss:0.545635 
## [217]    train-logloss:0.545169 
## [218]    train-logloss:0.544865 
## [219]    train-logloss:0.544405 
## [220]    train-logloss:0.544109 
## [221]    train-logloss:0.543808 
## [222]    train-logloss:0.543353 
## [223]    train-logloss:0.543054 
## [224]    train-logloss:0.542763 
## [225]    train-logloss:0.542313 
## [226]    train-logloss:0.542018 
## [227]    train-logloss:0.541731 
## [228]    train-logloss:0.541440 
## [229]    train-logloss:0.540994 
## [230]    train-logloss:0.540711 
## [231]    train-logloss:0.540271 
## [232]    train-logloss:0.539982 
## [233]    train-logloss:0.539703 
## [234]    train-logloss:0.539419 
## [235]    train-logloss:0.538983 
## [236]    train-logloss:0.538707 
## [237]    train-logloss:0.538426 
## [238]    train-logloss:0.537995 
## [239]    train-logloss:0.537717 
## [240]    train-logloss:0.537445 
## [241]    train-logloss:0.537019 
## [242]    train-logloss:0.536749 
## [243]    train-logloss:0.536475 
## [244]    train-logloss:0.536054 
## [245]    train-logloss:0.535788 
## [246]    train-logloss:0.535517 
## [247]    train-logloss:0.535101 
## [248]    train-logloss:0.534832 
## [249]    train-logloss:0.534570 
## [250]    train-logloss:0.534158 
## [251]    train-logloss:0.533892 
## [252]    train-logloss:0.533633 
## [253]    train-logloss:0.533226 
## [254]    train-logloss:0.532969 
## [255]    train-logloss:0.532708 
## [256]    train-logloss:0.532305 
## [257]    train-logloss:0.532052 
## [258]    train-logloss:0.531793 
## [259]    train-logloss:0.531395 
## [260]    train-logloss:0.531138 
## [261]    train-logloss:0.530888 
## [262]    train-logloss:0.530495 
## [263]    train-logloss:0.530247 
## [264]    train-logloss:0.529995 
## [265]    train-logloss:0.529605 
## [266]    train-logloss:0.529355 
## [267]    train-logloss:0.528971 
## [268]    train-logloss:0.528726 
## [269]    train-logloss:0.528478 
## [270]    train-logloss:0.528098 
## [271]    train-logloss:0.527856 
## [272]    train-logloss:0.527612 
## [273]    train-logloss:0.527374 
## [274]    train-logloss:0.526996 
## [275]    train-logloss:0.526755 
## [276]    train-logloss:0.526383 
## [277]    train-logloss:0.526146 
## [278]    train-logloss:0.525907 
## [279]    train-logloss:0.525675 
## [280]    train-logloss:0.525306 
## [281]    train-logloss:0.525070 
## [282]    train-logloss:0.524705 
## [283]    train-logloss:0.524475 
## [284]    train-logloss:0.524241 
## [285]    train-logloss:0.523881 
## [286]    train-logloss:0.523649 
## [287]    train-logloss:0.523422 
## [288]    train-logloss:0.523065 
## [289]    train-logloss:0.522836 
## [290]    train-logloss:0.522613 
## [291]    train-logloss:0.522260 
## [292]    train-logloss:0.522033 
## [293]    train-logloss:0.521808 
## [294]    train-logloss:0.521459 
## [295]    train-logloss:0.521239 
## [296]    train-logloss:0.521017 
## [297]    train-logloss:0.520670 
## [298]    train-logloss:0.520453 
## [299]    train-logloss:0.520232 
## [300]    train-logloss:0.519890 
## [301]    train-logloss:0.519672 
## [302]    train-logloss:0.519305 
## [303]    train-logloss:0.519090 
## [304]    train-logloss:0.518751 
## [305]    train-logloss:0.518392 
## [306]    train-logloss:0.518178 
## [307]    train-logloss:0.517965 
## [308]    train-logloss:0.517612 
## [309]    train-logloss:0.517400 
## [310]    train-logloss:0.517191 
## [311]    train-logloss:0.516858 
## [312]    train-logloss:0.516511 
## [313]    train-logloss:0.516304 
## [314]    train-logloss:0.516096 
## [315]    train-logloss:0.515890 
## [316]    train-logloss:0.515548 
## [317]    train-logloss:0.515333 
## [318]    train-logloss:0.515128 
## [319]    train-logloss:0.514924 
## [320]    train-logloss:0.514589 
## [321]    train-logloss:0.514387 
## [322]    train-logloss:0.514064 
## [323]    train-logloss:0.513734 
## [324]    train-logloss:0.513534 
## [325]    train-logloss:0.513325 
## [326]    train-logloss:0.513123 
## [327]    train-logloss:0.512917 
## [328]    train-logloss:0.512718 
## [329]    train-logloss:0.512397 
## [330]    train-logloss:0.512195 
## [331]    train-logloss:0.511997 
## [332]    train-logloss:0.511797 
## [333]    train-logloss:0.511602 
## [334]    train-logloss:0.511287 
## [335]    train-logloss:0.511088 
## [336]    train-logloss:0.510894 
## [337]    train-logloss:0.510697 
## [338]    train-logloss:0.510389 
## [339]    train-logloss:0.510196 
## [340]    train-logloss:0.510002 
## [341]    train-logloss:0.509698 
## [342]    train-logloss:0.509508 
## [343]    train-logloss:0.509317 
## [344]    train-logloss:0.509121 
## [345]    train-logloss:0.508933 
## [346]    train-logloss:0.508634 
## [347]    train-logloss:0.508446 
## [348]    train-logloss:0.508259 
## [349]    train-logloss:0.508072 
## [350]    train-logloss:0.507779 
## [351]    train-logloss:0.507594 
## [352]    train-logloss:0.507410 
## [353]    train-logloss:0.507228 
## [354]    train-logloss:0.506939 
## [355]    train-logloss:0.506757 
## [356]    train-logloss:0.506576 
## [357]    train-logloss:0.506384 
## [358]    train-logloss:0.506101 
## [359]    train-logloss:0.505922 
## [360]    train-logloss:0.505742 
## [361]    train-logloss:0.505463 
## [362]    train-logloss:0.505285 
## [363]    train-logloss:0.505109 
## [364]    train-logloss:0.504934 
## [365]    train-logloss:0.504658 
## [366]    train-logloss:0.504484 
## [367]    train-logloss:0.504310 
## [368]    train-logloss:0.504121 
## [369]    train-logloss:0.503948 
## [370]    train-logloss:0.503776 
## [371]    train-logloss:0.503507 
## [372]    train-logloss:0.503335 
## [373]    train-logloss:0.503166 
## [374]    train-logloss:0.502902 
## [375]    train-logloss:0.502734 
## [376]    train-logloss:0.502563 
## [377]    train-logloss:0.502377 
## [378]    train-logloss:0.502211 
## [379]    train-logloss:0.502043 
## [380]    train-logloss:0.501784 
## [381]    train-logloss:0.501617 
## [382]    train-logloss:0.501453 
## [383]    train-logloss:0.501198 
## [384]    train-logloss:0.501037 
## [385]    train-logloss:0.500871 
## [386]    train-logloss:0.500620 
## [387]    train-logloss:0.500460 
## [388]    train-logloss:0.500296 
## [389]    train-logloss:0.500049 
## [390]    train-logloss:0.499892 
## [391]    train-logloss:0.499730 
## [392]    train-logloss:0.499546 
## [393]    train-logloss:0.499386 
## [394]    train-logloss:0.499230 
## [395]    train-logloss:0.498989 
## [396]    train-logloss:0.498829 
## [397]    train-logloss:0.498675 
## [398]    train-logloss:0.498436 
## [399]    train-logloss:0.498279 
## [400]    train-logloss:0.498127 
## [401]    train-logloss:0.497946 
## [402]    train-logloss:0.497789 
## [403]    train-logloss:0.497640 
## [404]    train-logloss:0.497406 
## [405]    train-logloss:0.497258 
## [406]    train-logloss:0.497102 
## [407]    train-logloss:0.496923 
## [408]    train-logloss:0.496769 
## [409]    train-logloss:0.496539 
## [410]    train-logloss:0.496387 
## [411]    train-logloss:0.496103 
## [412]    train-logloss:0.495876 
## [413]    train-logloss:0.495725 
## [414]    train-logloss:0.495580 
## [415]    train-logloss:0.495356 
## [416]    train-logloss:0.495214 
## [417]    train-logloss:0.495064 
## [418]    train-logloss:0.494844 
## [419]    train-logloss:0.494702 
## [420]    train-logloss:0.494526 
## [421]    train-logloss:0.494385 
## [422]    train-logloss:0.494238 
## [423]    train-logloss:0.494099 
## [424]    train-logloss:0.493883 
## [425]    train-logloss:0.493745 
## [426]    train-logloss:0.493599 
## [427]    train-logloss:0.493322 
## [428]    train-logloss:0.493109 
## [429]    train-logloss:0.492935 
## [430]    train-logloss:0.492660 
## [431]    train-logloss:0.492451 
## [432]    train-logloss:0.492307 
## [433]    train-logloss:0.492172 
## [434]    train-logloss:0.492029 
## [435]    train-logloss:0.491857 
## [436]    train-logloss:0.491723 
## [437]    train-logloss:0.491517 
## [438]    train-logloss:0.491384 
## [439]    train-logloss:0.491243 
## [440]    train-logloss:0.490973 
## [441]    train-logloss:0.490769 
## [442]    train-logloss:0.490568 
## [443]    train-logloss:0.490436 
## [444]    train-logloss:0.490296 
## [445]    train-logloss:0.490165 
## [446]    train-logloss:0.489994 
## [447]    train-logloss:0.489855 
## [448]    train-logloss:0.489727 
## [449]    train-logloss:0.489529 
## [450]    train-logloss:0.489402 
## [451]    train-logloss:0.489276 
## [452]    train-logloss:0.489107 
## [453]    train-logloss:0.488838 
## [454]    train-logloss:0.488644 
## [455]    train-logloss:0.488505 
## [456]    train-logloss:0.488380 
## [457]    train-logloss:0.488188 
## [458]    train-logloss:0.488064 
## [459]    train-logloss:0.487927 
## [460]    train-logloss:0.487662 
## [461]    train-logloss:0.487473 
## [462]    train-logloss:0.487305 
## [463]    train-logloss:0.487044 
## [464]    train-logloss:0.486857 
## [465]    train-logloss:0.486721 
## [466]    train-logloss:0.486536 
## [467]    train-logloss:0.486413 
## [468]    train-logloss:0.486291 
## [469]    train-logloss:0.486125 
## [470]    train-logloss:0.485991 
## [471]    train-logloss:0.485869 
## [472]    train-logloss:0.485611 
## [473]    train-logloss:0.485429 
## [474]    train-logloss:0.485266 
## [475]    train-logloss:0.485133 
## [476]    train-logloss:0.484952 
## [477]    train-logloss:0.484832 
## [478]    train-logloss:0.484713 
## [479]    train-logloss:0.484551 
## [480]    train-logloss:0.484419 
## [481]    train-logloss:0.484163 
## [482]    train-logloss:0.483984 
## [483]    train-logloss:0.483853 
## [484]    train-logloss:0.483693 
## [485]    train-logloss:0.483575 
## [486]    train-logloss:0.483398 
## [487]    train-logloss:0.483282 
## [488]    train-logloss:0.483167 
## [489]    train-logloss:0.483008 
## [490]    train-logloss:0.482877 
## [491]    train-logloss:0.482625 
## [492]    train-logloss:0.482450 
## [493]    train-logloss:0.482276 
## [494]    train-logloss:0.482147 
## [495]    train-logloss:0.481990 
## [496]    train-logloss:0.481876 
## [497]    train-logloss:0.481622 
## [498]    train-logloss:0.481494 
## [499]    train-logloss:0.481323 
## [500]    train-logloss:0.481209 
## [501]    train-logloss:0.480959 
## [502]    train-logloss:0.480791 
## [503]    train-logloss:0.480635 
## [504]    train-logloss:0.480507 
## [505]    train-logloss:0.480261 
## [506]    train-logloss:0.480094 
## [507]    train-logloss:0.479966 
## [508]    train-logloss:0.479723 
## [509]    train-logloss:0.479558 
## [510]    train-logloss:0.479404 
## [511]    train-logloss:0.479278 
## [512]    train-logloss:0.479126 
## [513]    train-logloss:0.479013 
## [514]    train-logloss:0.478772 
## [515]    train-logloss:0.478647 
## [516]    train-logloss:0.478446 
## [517]    train-logloss:0.478335 
## [518]    train-logloss:0.478097 
## [519]    train-logloss:0.477898 
## [520]    train-logloss:0.477747 
## [521]    train-logloss:0.477511 
## [522]    train-logloss:0.477360 
## [523]    train-logloss:0.477164 
## [524]    train-logloss:0.476930 
## [525]    train-logloss:0.476820 
## [526]    train-logloss:0.476627 
## [527]    train-logloss:0.476396 
## [528]    train-logloss:0.476247 
## [529]    train-logloss:0.476122 
## [530]    train-logloss:0.475932 
## [531]    train-logloss:0.475701 
## [532]    train-logloss:0.475553 
## [533]    train-logloss:0.475445 
## [534]    train-logloss:0.475257 
## [535]    train-logloss:0.475029 
## [536]    train-logloss:0.474882 
## [537]    train-logloss:0.474775 
## [538]    train-logloss:0.474590 
## [539]    train-logloss:0.474365 
## [540]    train-logloss:0.474181 
## [541]    train-logloss:0.473958 
## [542]    train-logloss:0.473811 
## [543]    train-logloss:0.473706 
## [544]    train-logloss:0.473525 
## [545]    train-logloss:0.473304 
## [546]    train-logloss:0.473158 
## [547]    train-logloss:0.472979 
## [548]    train-logloss:0.472875 
## [549]    train-logloss:0.472656 
## [550]    train-logloss:0.472480 
## [551]    train-logloss:0.472261 
## [552]    train-logloss:0.472117 
## [553]    train-logloss:0.471943 
## [554]    train-logloss:0.471841 
## [555]    train-logloss:0.471624 
## [556]    train-logloss:0.471481 
## [557]    train-logloss:0.471309 
## [558]    train-logloss:0.471093 
## [559]    train-logloss:0.470993 
## [560]    train-logloss:0.470823 
## [561]    train-logloss:0.470609 
## [562]    train-logloss:0.470468 
## [563]    train-logloss:0.470300 
## [564]    train-logloss:0.470088 
## [565]    train-logloss:0.469947 
## [566]    train-logloss:0.469865 
## [567]    train-logloss:0.469654 
## [568]    train-logloss:0.469488 
## [569]    train-logloss:0.469349 
## [570]    train-logloss:0.469268 
## [571]    train-logloss:0.469105 
## [572]    train-logloss:0.468896 
## [573]    train-logloss:0.468757 
## [574]    train-logloss:0.468633 
## [575]    train-logloss:0.468471 
## [576]    train-logloss:0.468263 
## [577]    train-logloss:0.468126 
## [578]    train-logloss:0.467967 
## [579]    train-logloss:0.467887 
## [580]    train-logloss:0.467680 
## [581]    train-logloss:0.467543 
## [582]    train-logloss:0.467385 
## [583]    train-logloss:0.467180 
## [584]    train-logloss:0.467101 
## [585]    train-logloss:0.466966 
## [586]    train-logloss:0.466810 
## [587]    train-logloss:0.466606 
## [588]    train-logloss:0.466451 
## [589]    train-logloss:0.466374 
## [590]    train-logloss:0.466240 
## [591]    train-logloss:0.466037 
## [592]    train-logloss:0.465884 
## [593]    train-logloss:0.465751 
## [594]    train-logloss:0.465549 
## [595]    train-logloss:0.465398 
## [596]    train-logloss:0.465321 
## [597]    train-logloss:0.465121 
## [598]    train-logloss:0.464990 
## [599]    train-logloss:0.464840 
## [600]    train-logloss:0.464709 
## [601]    train-logloss:0.464484 
## [602]    train-logloss:0.464336 
## [603]    train-logloss:0.464206 
## [604]    train-logloss:0.463984 
## [605]    train-logloss:0.463837 
## [606]    train-logloss:0.463708 
## [607]    train-logloss:0.463563 
## [608]    train-logloss:0.463487 
## [609]    train-logloss:0.463267 
## [610]    train-logloss:0.463124 
## [611]    train-logloss:0.462995 
## [612]    train-logloss:0.462778 
## [613]    train-logloss:0.462636 
## [614]    train-logloss:0.462508 
## [615]    train-logloss:0.462367 
## [616]    train-logloss:0.462152 
## [617]    train-logloss:0.462025 
## [618]    train-logloss:0.461886 
## [619]    train-logloss:0.461811 
## [620]    train-logloss:0.461685 
## [621]    train-logloss:0.461472 
## [622]    train-logloss:0.461334 
## [623]    train-logloss:0.461209 
## [624]    train-logloss:0.460998 
## [625]    train-logloss:0.460862 
## [626]    train-logloss:0.460737 
## [627]    train-logloss:0.460602 
## [628]    train-logloss:0.460394 
## [629]    train-logloss:0.460260 
## [630]    train-logloss:0.460136 
## [631]    train-logloss:0.459931 
## [632]    train-logloss:0.459798 
## [633]    train-logloss:0.459675 
## [634]    train-logloss:0.459599 
## [635]    train-logloss:0.459467 
## [636]    train-logloss:0.459264 
## [637]    train-logloss:0.459141 
## [638]    train-logloss:0.459011 
## [639]    train-logloss:0.458889 
## [640]    train-logloss:0.458688 
## [641]    train-logloss:0.458558 
## [642]    train-logloss:0.458393 
## [643]    train-logloss:0.458264 
## [644]    train-logloss:0.458144 
## [645]    train-logloss:0.458068 
## [646]    train-logloss:0.457941 
## [647]    train-logloss:0.457778 
## [648]    train-logloss:0.457652 
## [649]    train-logloss:0.457532 
## [650]    train-logloss:0.457458 
## [651]    train-logloss:0.457339 
## [652]    train-logloss:0.457214 
## [653]    train-logloss:0.457013 
## [654]    train-logloss:0.456889 
## [655]    train-logloss:0.456728 
## [656]    train-logloss:0.456610 
## [657]    train-logloss:0.456411 
## [658]    train-logloss:0.456288 
## [659]    train-logloss:0.456171 
## [660]    train-logloss:0.455975 
## [661]    train-logloss:0.455853 
## [662]    train-logloss:0.455693 
## [663]    train-logloss:0.455576 
## [664]    train-logloss:0.455502 
## [665]    train-logloss:0.455381 
## [666]    train-logloss:0.455265 
## [667]    train-logloss:0.455145 
## [668]    train-logloss:0.454950 
## [669]    train-logloss:0.454835 
## [670]    train-logloss:0.454716 
## [671]    train-logloss:0.454558 
## [672]    train-logloss:0.454483 
## [673]    train-logloss:0.454369 
## [674]    train-logloss:0.454252 
## [675]    train-logloss:0.454059 
## [676]    train-logloss:0.453945 
## [677]    train-logloss:0.453828 
## [678]    train-logloss:0.453671 
## [679]    train-logloss:0.453555 
## [680]    train-logloss:0.453443 
## [681]    train-logloss:0.453369 
## [682]    train-logloss:0.453254 
## [683]    train-logloss:0.453062 
## [684]    train-logloss:0.452950 
## [685]    train-logloss:0.452836 
## [686]    train-logloss:0.452681 
## [687]    train-logloss:0.452492 
## [688]    train-logloss:0.452381 
## [689]    train-logloss:0.452267 
## [690]    train-logloss:0.452157 
## [691]    train-logloss:0.451969 
## [692]    train-logloss:0.451857 
## [693]    train-logloss:0.451702 
## [694]    train-logloss:0.451591 
## [695]    train-logloss:0.451481 
## [696]    train-logloss:0.451408 
## [697]    train-logloss:0.451297 
## [698]    train-logloss:0.451189 
## [699]    train-logloss:0.451002 
## [700]    train-logloss:0.450850 
## [701]    train-logloss:0.450740 
## [702]    train-logloss:0.450632 
## [703]    train-logloss:0.450523 
## [704]    train-logloss:0.450450 
## [705]    train-logloss:0.450343 
## [706]    train-logloss:0.450236 
## [707]    train-logloss:0.450050 
## [708]    train-logloss:0.449899 
## [709]    train-logloss:0.449793 
## [710]    train-logloss:0.449685 
## [711]    train-logloss:0.449613 
## [712]    train-logloss:0.449507 
## [713]    train-logloss:0.449226 
## [714]    train-logloss:0.449155 
## [715]    train-logloss:0.449006 
## [716]    train-logloss:0.448900 
## [717]    train-logloss:0.448795 
## [718]    train-logloss:0.448725 
## [719]    train-logloss:0.448540 
## [720]    train-logloss:0.448435 
## [721]    train-logloss:0.448330 
## [722]    train-logloss:0.448182 
## [723]    train-logloss:0.448078 
## [724]    train-logloss:0.447896 
## [725]    train-logloss:0.447791 
## [726]    train-logloss:0.447688 
## [727]    train-logloss:0.447411 
## [728]    train-logloss:0.447230 
## [729]    train-logloss:0.447083 
## [730]    train-logloss:0.446979 
## [731]    train-logloss:0.446876 
## [732]    train-logloss:0.446806 
## [733]    train-logloss:0.446704 
## [734]    train-logloss:0.446601 
## [735]    train-logloss:0.446422 
## [736]    train-logloss:0.446276 
## [737]    train-logloss:0.446175 
## [738]    train-logloss:0.446105 
## [739]    train-logloss:0.446003 
## [740]    train-logloss:0.445902 
## [741]    train-logloss:0.445834 
## [742]    train-logloss:0.445690 
## [743]    train-logloss:0.445589 
## [744]    train-logloss:0.445489 
## [745]    train-logloss:0.445300 
## [746]    train-logloss:0.445199 
## [747]    train-logloss:0.445100 
## [748]    train-logloss:0.445033 
## [749]    train-logloss:0.444847 
## [750]    train-logloss:0.444667 
## [751]    train-logloss:0.444567 
## [752]    train-logloss:0.444469 
## [753]    train-logloss:0.444292 
## [754]    train-logloss:0.444192 
## [755]    train-logloss:0.444095 
## [756]    train-logloss:0.443821 
## [757]    train-logloss:0.443678 
## [758]    train-logloss:0.443611 
## [759]    train-logloss:0.443514 
## [760]    train-logloss:0.443448 
## [761]    train-logloss:0.443307 
## [762]    train-logloss:0.443208 
## [763]    train-logloss:0.443112 
## [764]    train-logloss:0.442928 
## [765]    train-logloss:0.442830 
## [766]    train-logloss:0.442654 
## [767]    train-logloss:0.442559 
## [768]    train-logloss:0.442385 
## [769]    train-logloss:0.442287 
## [770]    train-logloss:0.442192 
## [771]    train-logloss:0.442127 
## [772]    train-logloss:0.441986 
## [773]    train-logloss:0.441892 
## [774]    train-logloss:0.441795 
## [775]    train-logloss:0.441701 
## [776]    train-logloss:0.441637 
## [777]    train-logloss:0.441456 
## [778]    train-logloss:0.441359 
## [779]    train-logloss:0.441181 
## [780]    train-logloss:0.441009 
## [781]    train-logloss:0.440912 
## [782]    train-logloss:0.440819 
## [783]    train-logloss:0.440649 
## [784]    train-logloss:0.440553 
## [785]    train-logloss:0.440461 
## [786]    train-logloss:0.440322 
## [787]    train-logloss:0.440230 
## [788]    train-logloss:0.440167 
## [789]    train-logloss:0.440071 
## [790]    train-logloss:0.440008 
## [791]    train-logloss:0.439917 
## [792]    train-logloss:0.439781 
## [793]    train-logloss:0.439685 
## [794]    train-logloss:0.439510 
## [795]    train-logloss:0.439341 
## [796]    train-logloss:0.439250 
## [797]    train-logloss:0.439156 
## [798]    train-logloss:0.438887 
## [799]    train-logloss:0.438721 
## [800]    train-logloss:0.438631 
## [801]    train-logloss:0.438495 
## [802]    train-logloss:0.438432 
## [803]    train-logloss:0.438343 
## [804]    train-logloss:0.438248 
## [805]    train-logloss:0.438083 
## [806]    train-logloss:0.437989 
## [807]    train-logloss:0.437900 
## [808]    train-logloss:0.437839 
## [809]    train-logloss:0.437704 
## [810]    train-logloss:0.437616 
## [811]    train-logloss:0.437443 
## [812]    train-logloss:0.437350 
## [813]    train-logloss:0.437289 
## [814]    train-logloss:0.437201 
## [815]    train-logloss:0.437031 
## [816]    train-logloss:0.436867 
## [817]    train-logloss:0.436774 
## [818]    train-logloss:0.436687 
## [819]    train-logloss:0.436526 
## [820]    train-logloss:0.436433 
## [821]    train-logloss:0.436347 
## [822]    train-logloss:0.436287 
## [823]    train-logloss:0.436153 
## [824]    train-logloss:0.436062 
## [825]    train-logloss:0.435975 
## [826]    train-logloss:0.435807 
## [827]    train-logloss:0.435716 
## [828]    train-logloss:0.435657 
## [829]    train-logloss:0.435491 
## [830]    train-logloss:0.435331 
## [831]    train-logloss:0.435245 
## [832]    train-logloss:0.435155 
## [833]    train-logloss:0.434995 
## [834]    train-logloss:0.434862 
## [835]    train-logloss:0.434777 
## [836]    train-logloss:0.434687 
## [837]    train-logloss:0.434481 
## [838]    train-logloss:0.434391 
## [839]    train-logloss:0.434333 
## [840]    train-logloss:0.434248 
## [841]    train-logloss:0.434117 
## [842]    train-logloss:0.433914 
## [843]    train-logloss:0.433830 
## [844]    train-logloss:0.433665 
## [845]    train-logloss:0.433608 
## [846]    train-logloss:0.433518 
## [847]    train-logloss:0.433317 
## [848]    train-logloss:0.433155 
## [849]    train-logloss:0.433066 
## [850]    train-logloss:0.432983 
## [851]    train-logloss:0.432926 
## [852]    train-logloss:0.432765 
## [853]    train-logloss:0.432676 
## [854]    train-logloss:0.432477 
## [855]    train-logloss:0.432318 
## [856]    train-logloss:0.432229 
## [857]    train-logloss:0.432147 
## [858]    train-logloss:0.431949 
## [859]    train-logloss:0.431893 
## [860]    train-logloss:0.431805 
## [861]    train-logloss:0.431647 
## [862]    train-logloss:0.431452 
## [863]    train-logloss:0.431369 
## [864]    train-logloss:0.431314 
## [865]    train-logloss:0.431158 
## [866]    train-logloss:0.431070 
## [867]    train-logloss:0.430876 
## [868]    train-logloss:0.430789 
## [869]    train-logloss:0.430734 
## [870]    train-logloss:0.430579 
## [871]    train-logloss:0.430497 
## [872]    train-logloss:0.430305 
## [873]    train-logloss:0.430218 
## [874]    train-logloss:0.430088 
## [875]    train-logloss:0.429934 
## [876]    train-logloss:0.429853 
## [877]    train-logloss:0.429663 
## [878]    train-logloss:0.429576 
## [879]    train-logloss:0.429522 
## [880]    train-logloss:0.429369 
## [881]    train-logloss:0.429283 
## [882]    train-logloss:0.429202 
## [883]    train-logloss:0.429013 
## [884]    train-logloss:0.428862 
## [885]    train-logloss:0.428776 
## [886]    train-logloss:0.428723 
## [887]    train-logloss:0.428536 
## [888]    train-logloss:0.428408 
## [889]    train-logloss:0.428321 
## [890]    train-logloss:0.428268 
## [891]    train-logloss:0.428118 
## [892]    train-logloss:0.427933 
## [893]    train-logloss:0.427848 
## [894]    train-logloss:0.427699 
## [895]    train-logloss:0.427619 
## [896]    train-logloss:0.427534 
## [897]    train-logloss:0.427351 
## [898]    train-logloss:0.427299 
## [899]    train-logloss:0.427151 
## [900]    train-logloss:0.427066 
## [901]    train-logloss:0.426885 
## [902]    train-logloss:0.426833 
## [903]    train-logloss:0.426749 
## [904]    train-logloss:0.426602 
## [905]    train-logloss:0.426422 
## [906]    train-logloss:0.426339 
## [907]    train-logloss:0.426210 
## [908]    train-logloss:0.426065 
## [909]    train-logloss:0.426013 
## [910]    train-logloss:0.425933 
## [911]    train-logloss:0.425850 
## [912]    train-logloss:0.425771 
## [913]    train-logloss:0.425720 
## [914]    train-logloss:0.425470 
## [915]    train-logloss:0.425392 
## [916]    train-logloss:0.425309 
## [917]    train-logloss:0.425156 
## [918]    train-logloss:0.425029 
## [919]    train-logloss:0.424978 
## [920]    train-logloss:0.424898 
## [921]    train-logloss:0.424754 
## [922]    train-logloss:0.424671 
## [923]    train-logloss:0.424545 
## [924]    train-logloss:0.424368 
## [925]    train-logloss:0.424288 
## [926]    train-logloss:0.424145 
## [927]    train-logloss:0.424062 
## [928]    train-logloss:0.424011 
## [929]    train-logloss:0.423929 
## [930]    train-logloss:0.423852 
## [931]    train-logloss:0.423802 
## [932]    train-logloss:0.423660 
## [933]    train-logloss:0.423535 
## [934]    train-logloss:0.423453 
## [935]    train-logloss:0.423277 
## [936]    train-logloss:0.423136 
## [937]    train-logloss:0.423060 
## [938]    train-logloss:0.422978 
## [939]    train-logloss:0.422829 
## [940]    train-logloss:0.422750 
## [941]    train-logloss:0.422625 
## [942]    train-logloss:0.422576 
## [943]    train-logloss:0.422403 
## [944]    train-logloss:0.422322 
## [945]    train-logloss:0.422181 
## [946]    train-logloss:0.422010 
## [947]    train-logloss:0.421935 
## [948]    train-logloss:0.421854 
## [949]    train-logloss:0.421805 
## [950]    train-logloss:0.421665 
## [951]    train-logloss:0.421591 
## [952]    train-logloss:0.421510 
## [953]    train-logloss:0.421387 
## [954]    train-logloss:0.421308 
## [955]    train-logloss:0.421259 
## [956]    train-logloss:0.421180 
## [957]    train-logloss:0.421040 
## [958]    train-logloss:0.420992 
## [959]    train-logloss:0.420912 
## [960]    train-logloss:0.420743 
## [961]    train-logloss:0.420670 
## [962]    train-logloss:0.420532 
## [963]    train-logloss:0.420385 
## [964]    train-logloss:0.420262 
## [965]    train-logloss:0.420183 
## [966]    train-logloss:0.420105 
## [967]    train-logloss:0.419860 
## [968]    train-logloss:0.419782 
## [969]    train-logloss:0.419645 
## [970]    train-logloss:0.419478 
## [971]    train-logloss:0.419399 
## [972]    train-logloss:0.419278 
## [973]    train-logloss:0.419229 
## [974]    train-logloss:0.419064 
## [975]    train-logloss:0.418986 
## [976]    train-logloss:0.418913 
## [977]    train-logloss:0.418776 
## [978]    train-logloss:0.418698 
## [979]    train-logloss:0.418626 
## [980]    train-logloss:0.418579 
## [981]    train-logloss:0.418508 
## [982]    train-logloss:0.418387 
## [983]    train-logloss:0.418309 
## [984]    train-logloss:0.418173 
## [985]    train-logloss:0.418096 
## [986]    train-logloss:0.418049 
## [987]    train-logloss:0.417905 
## [988]    train-logloss:0.417827 
## [989]    train-logloss:0.417757 
## [990]    train-logloss:0.417614 
## [991]    train-logloss:0.417494 
## [992]    train-logloss:0.417417 
## [993]    train-logloss:0.417339 
## [994]    train-logloss:0.417292 
## [995]    train-logloss:0.417158 
## [996]    train-logloss:0.417081 
## [997]    train-logloss:0.417034 
## [998]    train-logloss:0.416892 
## [999]    train-logloss:0.416773 
## [1000]   train-logloss:0.416697

## Make predictions on test data 
moklas1.test.matrix<-data.matrix(moklas1.test[,-10])
promo.test<-as.matrix(as.factor(as.character(moklas1.test$promo)))
predicted <- predict(xgbModel1,moklas1.test.matrix )

predicted <- ifelse(predicted > 0.5 , 1,0)

## Create confusion matrix

confusionMatrix(table(predicted = predicted, actual = promo.test))

## Confusion Matrix and Statistics
## 
##          actual
## predicted  0  1
##         0 33  7
##         1 14 17
##                                           
##                Accuracy : 0.7042          
##                  95% CI : (0.5841, 0.8067)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.2681          
##                                           
##                   Kappa : 0.3831          
##                                           
##  Mcnemar's Test P-Value : 0.1904          
##                                           
##             Sensitivity : 0.7021          
##             Specificity : 0.7083          
##          Pos Pred Value : 0.8250          
##          Neg Pred Value : 0.5484          
##              Prevalence : 0.6620          
##          Detection Rate : 0.4648          
##    Detection Prevalence : 0.5634          
##       Balanced Accuracy : 0.7052          
##                                           
##        'Positive' Class : 0               
## 

ROS

library(xgboost)
up.train.matrix<-data.matrix(up_train[,-10])
promo2<-as.matrix(as.factor(as.character(up_train$promo)))
xgbModel2 <- xgboost(data = up.train.matrix, 
                    label = promo2,
                    nrounds = 1000,
                    max_depth = 2,
                    eta = 0.01,
                    objective = "binary:logistic")

## [1]  train-logloss:0.683399 
## [2]  train-logloss:0.673840 
## [3]  train-logloss:0.664465 
## [4]  train-logloss:0.655269 
## [5]  train-logloss:0.646246 
## [6]  train-logloss:0.637392 
## [7]  train-logloss:0.628701 
## [8]  train-logloss:0.620171 
## [9]  train-logloss:0.611796 
## [10] train-logloss:0.603573 
## [11] train-logloss:0.595496 
## [12] train-logloss:0.587564 
## [13] train-logloss:0.579772 
## [14] train-logloss:0.572115 
## [15] train-logloss:0.564592 
## [16] train-logloss:0.557199 
## [17] train-logloss:0.549933 
## [18] train-logloss:0.542790 
## [19] train-logloss:0.535767 
## [20] train-logloss:0.528862 
## [21] train-logloss:0.522072 
## [22] train-logloss:0.515395 
## [23] train-logloss:0.508827 
## [24] train-logloss:0.502367 
## [25] train-logloss:0.496011 
## [26] train-logloss:0.489757 
## [27] train-logloss:0.483604 
## [28] train-logloss:0.477549 
## [29] train-logloss:0.471589 
## [30] train-logloss:0.465724 
## [31] train-logloss:0.459949 
## [32] train-logloss:0.454265 
## [33] train-logloss:0.448669 
## [34] train-logloss:0.443159 
## [35] train-logloss:0.437733 
## [36] train-logloss:0.432389 
## [37] train-logloss:0.427127 
## [38] train-logloss:0.421943 
## [39] train-logloss:0.416837 
## [40] train-logloss:0.411808 
## [41] train-logloss:0.406852 
## [42] train-logloss:0.401970 
## [43] train-logloss:0.397160 
## [44] train-logloss:0.392420 
## [45] train-logloss:0.387749 
## [46] train-logloss:0.383145 
## [47] train-logloss:0.378608 
## [48] train-logloss:0.374136 
## [49] train-logloss:0.369727 
## [50] train-logloss:0.365382 
## [51] train-logloss:0.361098 
## [52] train-logloss:0.356874 
## [53] train-logloss:0.352710 
## [54] train-logloss:0.348603 
## [55] train-logloss:0.344554 
## [56] train-logloss:0.340561 
## [57] train-logloss:0.336624 
## [58] train-logloss:0.332740 
## [59] train-logloss:0.328910 
## [60] train-logloss:0.325132 
## [61] train-logloss:0.321405 
## [62] train-logloss:0.317729 
## [63] train-logloss:0.314102 
## [64] train-logloss:0.310525 
## [65] train-logloss:0.306995 
## [66] train-logloss:0.303512 
## [67] train-logloss:0.300076 
## [68] train-logloss:0.296685 
## [69] train-logloss:0.293340 
## [70] train-logloss:0.290038 
## [71] train-logloss:0.286780 
## [72] train-logloss:0.283564 
## [73] train-logloss:0.280390 
## [74] train-logloss:0.277258 
## [75] train-logloss:0.274166 
## [76] train-logloss:0.271114 
## [77] train-logloss:0.268102 
## [78] train-logloss:0.265128 
## [79] train-logloss:0.262192 
## [80] train-logloss:0.259294 
## [81] train-logloss:0.256433 
## [82] train-logloss:0.253608 
## [83] train-logloss:0.250819 
## [84] train-logloss:0.248065 
## [85] train-logloss:0.245346 
## [86] train-logloss:0.242661 
## [87] train-logloss:0.240009 
## [88] train-logloss:0.237391 
## [89] train-logloss:0.234805 
## [90] train-logloss:0.232252 
## [91] train-logloss:0.229730 
## [92] train-logloss:0.227239 
## [93] train-logloss:0.224780 
## [94] train-logloss:0.222350 
## [95] train-logloss:0.219950 
## [96] train-logloss:0.217580 
## [97] train-logloss:0.215239 
## [98] train-logloss:0.212926 
## [99] train-logloss:0.210641 
## [100]    train-logloss:0.208384 
## [101]    train-logloss:0.206154 
## [102]    train-logloss:0.203952 
## [103]    train-logloss:0.201776 
## [104]    train-logloss:0.199626 
## [105]    train-logloss:0.197501 
## [106]    train-logloss:0.195403 
## [107]    train-logloss:0.193329 
## [108]    train-logloss:0.191280 
## [109]    train-logloss:0.189256 
## [110]    train-logloss:0.187255 
## [111]    train-logloss:0.185278 
## [112]    train-logloss:0.183325 
## [113]    train-logloss:0.181395 
## [114]    train-logloss:0.179487 
## [115]    train-logloss:0.177602 
## [116]    train-logloss:0.175739 
## [117]    train-logloss:0.173898 
## [118]    train-logloss:0.172079 
## [119]    train-logloss:0.170280 
## [120]    train-logloss:0.168503 
## [121]    train-logloss:0.166746 
## [122]    train-logloss:0.165010 
## [123]    train-logloss:0.163294 
## [124]    train-logloss:0.161597 
## [125]    train-logloss:0.159921 
## [126]    train-logloss:0.158263 
## [127]    train-logloss:0.156625 
## [128]    train-logloss:0.155006 
## [129]    train-logloss:0.153405 
## [130]    train-logloss:0.151822 
## [131]    train-logloss:0.150258 
## [132]    train-logloss:0.148712 
## [133]    train-logloss:0.147183 
## [134]    train-logloss:0.145671 
## [135]    train-logloss:0.144177 
## [136]    train-logloss:0.142700 
## [137]    train-logloss:0.141239 
## [138]    train-logloss:0.139795 
## [139]    train-logloss:0.138368 
## [140]    train-logloss:0.136956 
## [141]    train-logloss:0.135560 
## [142]    train-logloss:0.134180 
## [143]    train-logloss:0.132816 
## [144]    train-logloss:0.131467 
## [145]    train-logloss:0.130133 
## [146]    train-logloss:0.128814 
## [147]    train-logloss:0.127509 
## [148]    train-logloss:0.126220 
## [149]    train-logloss:0.124944 
## [150]    train-logloss:0.123683 
## [151]    train-logloss:0.122436 
## [152]    train-logloss:0.121203 
## [153]    train-logloss:0.119983 
## [154]    train-logloss:0.118777 
## [155]    train-logloss:0.117584 
## [156]    train-logloss:0.116405 
## [157]    train-logloss:0.115238 
## [158]    train-logloss:0.114084 
## [159]    train-logloss:0.112944 
## [160]    train-logloss:0.111815 
## [161]    train-logloss:0.110699 
## [162]    train-logloss:0.109595 
## [163]    train-logloss:0.108504 
## [164]    train-logloss:0.107424 
## [165]    train-logloss:0.106356 
## [166]    train-logloss:0.105300 
## [167]    train-logloss:0.104255 
## [168]    train-logloss:0.103222 
## [169]    train-logloss:0.102200 
## [170]    train-logloss:0.101189 
## [171]    train-logloss:0.100189 
## [172]    train-logloss:0.099200 
## [173]    train-logloss:0.098221 
## [174]    train-logloss:0.097253 
## [175]    train-logloss:0.096296 
## [176]    train-logloss:0.095349 
## [177]    train-logloss:0.094412 
## [178]    train-logloss:0.093486 
## [179]    train-logloss:0.092569 
## [180]    train-logloss:0.091662 
## [181]    train-logloss:0.090765 
## [182]    train-logloss:0.089878 
## [183]    train-logloss:0.089000 
## [184]    train-logloss:0.088132 
## [185]    train-logloss:0.087273 
## [186]    train-logloss:0.086423 
## [187]    train-logloss:0.085582 
## [188]    train-logloss:0.084750 
## [189]    train-logloss:0.083927 
## [190]    train-logloss:0.083113 
## [191]    train-logloss:0.082308 
## [192]    train-logloss:0.081511 
## [193]    train-logloss:0.080723 
## [194]    train-logloss:0.079942 
## [195]    train-logloss:0.079171 
## [196]    train-logloss:0.078407 
## [197]    train-logloss:0.077652 
## [198]    train-logloss:0.076904 
## [199]    train-logloss:0.076165 
## [200]    train-logloss:0.075433 
## [201]    train-logloss:0.074709 
## [202]    train-logloss:0.073993 
## [203]    train-logloss:0.073284 
## [204]    train-logloss:0.072583 
## [205]    train-logloss:0.071889 
## [206]    train-logloss:0.071203 
## [207]    train-logloss:0.070523 
## [208]    train-logloss:0.069851 
## [209]    train-logloss:0.069186 
## [210]    train-logloss:0.068528 
## [211]    train-logloss:0.067877 
## [212]    train-logloss:0.067232 
## [213]    train-logloss:0.066595 
## [214]    train-logloss:0.065964 
## [215]    train-logloss:0.065340 
## [216]    train-logloss:0.064722 
## [217]    train-logloss:0.064110 
## [218]    train-logloss:0.063505 
## [219]    train-logloss:0.062907 
## [220]    train-logloss:0.062314 
## [221]    train-logloss:0.061728 
## [222]    train-logloss:0.061148 
## [223]    train-logloss:0.060574 
## [224]    train-logloss:0.060006 
## [225]    train-logloss:0.059444 
## [226]    train-logloss:0.058887 
## [227]    train-logloss:0.058337 
## [228]    train-logloss:0.057792 
## [229]    train-logloss:0.057252 
## [230]    train-logloss:0.056719 
## [231]    train-logloss:0.056191 
## [232]    train-logloss:0.055668 
## [233]    train-logloss:0.055151 
## [234]    train-logloss:0.054639 
## [235]    train-logloss:0.054132 
## [236]    train-logloss:0.053631 
## [237]    train-logloss:0.053134 
## [238]    train-logloss:0.052643 
## [239]    train-logloss:0.052157 
## [240]    train-logloss:0.051676 
## [241]    train-logloss:0.051200 
## [242]    train-logloss:0.050729 
## [243]    train-logloss:0.050262 
## [244]    train-logloss:0.049801 
## [245]    train-logloss:0.049344 
## [246]    train-logloss:0.048892 
## [247]    train-logloss:0.048444 
## [248]    train-logloss:0.048001 
## [249]    train-logloss:0.047563 
## [250]    train-logloss:0.047129 
## [251]    train-logloss:0.046700 
## [252]    train-logloss:0.046274 
## [253]    train-logloss:0.045854 
## [254]    train-logloss:0.045437 
## [255]    train-logloss:0.045025 
## [256]    train-logloss:0.044617 
## [257]    train-logloss:0.044213 
## [258]    train-logloss:0.043813 
## [259]    train-logloss:0.043418 
## [260]    train-logloss:0.043026 
## [261]    train-logloss:0.042639 
## [262]    train-logloss:0.042255 
## [263]    train-logloss:0.041875 
## [264]    train-logloss:0.041499 
## [265]    train-logloss:0.041127 
## [266]    train-logloss:0.040758 
## [267]    train-logloss:0.040394 
## [268]    train-logloss:0.040033 
## [269]    train-logloss:0.039676 
## [270]    train-logloss:0.039322 
## [271]    train-logloss:0.038972 
## [272]    train-logloss:0.038625 
## [273]    train-logloss:0.038282 
## [274]    train-logloss:0.037942 
## [275]    train-logloss:0.037606 
## [276]    train-logloss:0.037273 
## [277]    train-logloss:0.036944 
## [278]    train-logloss:0.036618 
## [279]    train-logloss:0.036295 
## [280]    train-logloss:0.035976 
## [281]    train-logloss:0.035659 
## [282]    train-logloss:0.035346 
## [283]    train-logloss:0.035036 
## [284]    train-logloss:0.034729 
## [285]    train-logloss:0.034425 
## [286]    train-logloss:0.034124 
## [287]    train-logloss:0.033826 
## [288]    train-logloss:0.033532 
## [289]    train-logloss:0.033240 
## [290]    train-logloss:0.032951 
## [291]    train-logloss:0.032665 
## [292]    train-logloss:0.032382 
## [293]    train-logloss:0.032101 
## [294]    train-logloss:0.031824 
## [295]    train-logloss:0.031549 
## [296]    train-logloss:0.031277 
## [297]    train-logloss:0.031007 
## [298]    train-logloss:0.030741 
## [299]    train-logloss:0.030477 
## [300]    train-logloss:0.030215 
## [301]    train-logloss:0.029957 
## [302]    train-logloss:0.029700 
## [303]    train-logloss:0.029447 
## [304]    train-logloss:0.029195 
## [305]    train-logloss:0.028947 
## [306]    train-logloss:0.028701 
## [307]    train-logloss:0.028457 
## [308]    train-logloss:0.028215 
## [309]    train-logloss:0.027977 
## [310]    train-logloss:0.027740 
## [311]    train-logloss:0.027506 
## [312]    train-logloss:0.027274 
## [313]    train-logloss:0.027044 
## [314]    train-logloss:0.026817 
## [315]    train-logloss:0.026592 
## [316]    train-logloss:0.026369 
## [317]    train-logloss:0.026148 
## [318]    train-logloss:0.025929 
## [319]    train-logloss:0.025713 
## [320]    train-logloss:0.025499 
## [321]    train-logloss:0.025286 
## [322]    train-logloss:0.025076 
## [323]    train-logloss:0.024868 
## [324]    train-logloss:0.024662 
## [325]    train-logloss:0.024458 
## [326]    train-logloss:0.024256 
## [327]    train-logloss:0.024056 
## [328]    train-logloss:0.023858 
## [329]    train-logloss:0.023662 
## [330]    train-logloss:0.023468 
## [331]    train-logloss:0.023276 
## [332]    train-logloss:0.023085 
## [333]    train-logloss:0.022897 
## [334]    train-logloss:0.022710 
## [335]    train-logloss:0.022525 
## [336]    train-logloss:0.022342 
## [337]    train-logloss:0.022161 
## [338]    train-logloss:0.021981 
## [339]    train-logloss:0.021803 
## [340]    train-logloss:0.021627 
## [341]    train-logloss:0.021453 
## [342]    train-logloss:0.021280 
## [343]    train-logloss:0.021109 
## [344]    train-logloss:0.020940 
## [345]    train-logloss:0.020772 
## [346]    train-logloss:0.020606 
## [347]    train-logloss:0.020442 
## [348]    train-logloss:0.020279 
## [349]    train-logloss:0.020117 
## [350]    train-logloss:0.019958 
## [351]    train-logloss:0.019799 
## [352]    train-logloss:0.019643 
## [353]    train-logloss:0.019487 
## [354]    train-logloss:0.019334 
## [355]    train-logloss:0.019181 
## [356]    train-logloss:0.019031 
## [357]    train-logloss:0.018881 
## [358]    train-logloss:0.018733 
## [359]    train-logloss:0.018587 
## [360]    train-logloss:0.018442 
## [361]    train-logloss:0.018298 
## [362]    train-logloss:0.018156 
## [363]    train-logloss:0.018015 
## [364]    train-logloss:0.017875 
## [365]    train-logloss:0.017737 
## [366]    train-logloss:0.017600 
## [367]    train-logloss:0.017464 
## [368]    train-logloss:0.017330 
## [369]    train-logloss:0.017197 
## [370]    train-logloss:0.017065 
## [371]    train-logloss:0.016935 
## [372]    train-logloss:0.016805 
## [373]    train-logloss:0.016677 
## [374]    train-logloss:0.016550 
## [375]    train-logloss:0.016424 
## [376]    train-logloss:0.016300 
## [377]    train-logloss:0.016177 
## [378]    train-logloss:0.016054 
## [379]    train-logloss:0.015933 
## [380]    train-logloss:0.015814 
## [381]    train-logloss:0.015695 
## [382]    train-logloss:0.015577 
## [383]    train-logloss:0.015461 
## [384]    train-logloss:0.015345 
## [385]    train-logloss:0.015231 
## [386]    train-logloss:0.015118 
## [387]    train-logloss:0.015005 
## [388]    train-logloss:0.014894 
## [389]    train-logloss:0.014784 
## [390]    train-logloss:0.014675 
## [391]    train-logloss:0.014567 
## [392]    train-logloss:0.014460 
## [393]    train-logloss:0.014354 
## [394]    train-logloss:0.014249 
## [395]    train-logloss:0.014145 
## [396]    train-logloss:0.014041 
## [397]    train-logloss:0.013939 
## [398]    train-logloss:0.013838 
## [399]    train-logloss:0.013738 
## [400]    train-logloss:0.013638 
## [401]    train-logloss:0.013540 
## [402]    train-logloss:0.013442 
## [403]    train-logloss:0.013346 
## [404]    train-logloss:0.013250 
## [405]    train-logloss:0.013155 
## [406]    train-logloss:0.013061 
## [407]    train-logloss:0.012968 
## [408]    train-logloss:0.012876 
## [409]    train-logloss:0.012784 
## [410]    train-logloss:0.012694 
## [411]    train-logloss:0.012604 
## [412]    train-logloss:0.012515 
## [413]    train-logloss:0.012427 
## [414]    train-logloss:0.012340 
## [415]    train-logloss:0.012253 
## [416]    train-logloss:0.012167 
## [417]    train-logloss:0.012082 
## [418]    train-logloss:0.011998 
## [419]    train-logloss:0.011915 
## [420]    train-logloss:0.011832 
## [421]    train-logloss:0.011750 
## [422]    train-logloss:0.011669 
## [423]    train-logloss:0.011589 
## [424]    train-logloss:0.011509 
## [425]    train-logloss:0.011430 
## [426]    train-logloss:0.011352 
## [427]    train-logloss:0.011274 
## [428]    train-logloss:0.011197 
## [429]    train-logloss:0.011121 
## [430]    train-logloss:0.011045 
## [431]    train-logloss:0.010971 
## [432]    train-logloss:0.010896 
## [433]    train-logloss:0.010823 
## [434]    train-logloss:0.010750 
## [435]    train-logloss:0.010678 
## [436]    train-logloss:0.010606 
## [437]    train-logloss:0.010536 
## [438]    train-logloss:0.010465 
## [439]    train-logloss:0.010396 
## [440]    train-logloss:0.010327 
## [441]    train-logloss:0.010258 
## [442]    train-logloss:0.010190 
## [443]    train-logloss:0.010123 
## [444]    train-logloss:0.010057 
## [445]    train-logloss:0.009990 
## [446]    train-logloss:0.009925 
## [447]    train-logloss:0.009860 
## [448]    train-logloss:0.009796 
## [449]    train-logloss:0.009732 
## [450]    train-logloss:0.009669 
## [451]    train-logloss:0.009606 
## [452]    train-logloss:0.009544 
## [453]    train-logloss:0.009483 
## [454]    train-logloss:0.009422 
## [455]    train-logloss:0.009361 
## [456]    train-logloss:0.009301 
## [457]    train-logloss:0.009242 
## [458]    train-logloss:0.009183 
## [459]    train-logloss:0.009125 
## [460]    train-logloss:0.009067 
## [461]    train-logloss:0.009009 
## [462]    train-logloss:0.008952 
## [463]    train-logloss:0.008896 
## [464]    train-logloss:0.008840 
## [465]    train-logloss:0.008785 
## [466]    train-logloss:0.008730 
## [467]    train-logloss:0.008675 
## [468]    train-logloss:0.008621 
## [469]    train-logloss:0.008568 
## [470]    train-logloss:0.008515 
## [471]    train-logloss:0.008462 
## [472]    train-logloss:0.008410 
## [473]    train-logloss:0.008358 
## [474]    train-logloss:0.008307 
## [475]    train-logloss:0.008256 
## [476]    train-logloss:0.008206 
## [477]    train-logloss:0.008156 
## [478]    train-logloss:0.008106 
## [479]    train-logloss:0.008057 
## [480]    train-logloss:0.008008 
## [481]    train-logloss:0.007960 
## [482]    train-logloss:0.007912 
## [483]    train-logloss:0.007864 
## [484]    train-logloss:0.007817 
## [485]    train-logloss:0.007770 
## [486]    train-logloss:0.007724 
## [487]    train-logloss:0.007678 
## [488]    train-logloss:0.007633 
## [489]    train-logloss:0.007587 
## [490]    train-logloss:0.007542 
## [491]    train-logloss:0.007498 
## [492]    train-logloss:0.007454 
## [493]    train-logloss:0.007410 
## [494]    train-logloss:0.007367 
## [495]    train-logloss:0.007324 
## [496]    train-logloss:0.007281 
## [497]    train-logloss:0.007239 
## [498]    train-logloss:0.007197 
## [499]    train-logloss:0.007155 
## [500]    train-logloss:0.007114 
## [501]    train-logloss:0.007073 
## [502]    train-logloss:0.007033 
## [503]    train-logloss:0.006992 
## [504]    train-logloss:0.006952 
## [505]    train-logloss:0.006913 
## [506]    train-logloss:0.006874 
## [507]    train-logloss:0.006835 
## [508]    train-logloss:0.006796 
## [509]    train-logloss:0.006758 
## [510]    train-logloss:0.006720 
## [511]    train-logloss:0.006682 
## [512]    train-logloss:0.006645 
## [513]    train-logloss:0.006607 
## [514]    train-logloss:0.006571 
## [515]    train-logloss:0.006534 
## [516]    train-logloss:0.006498 
## [517]    train-logloss:0.006462 
## [518]    train-logloss:0.006426 
## [519]    train-logloss:0.006391 
## [520]    train-logloss:0.006356 
## [521]    train-logloss:0.006321 
## [522]    train-logloss:0.006287 
## [523]    train-logloss:0.006252 
## [524]    train-logloss:0.006218 
## [525]    train-logloss:0.006185 
## [526]    train-logloss:0.006151 
## [527]    train-logloss:0.006118 
## [528]    train-logloss:0.006085 
## [529]    train-logloss:0.006052 
## [530]    train-logloss:0.006020 
## [531]    train-logloss:0.005988 
## [532]    train-logloss:0.005956 
## [533]    train-logloss:0.005924 
## [534]    train-logloss:0.005893 
## [535]    train-logloss:0.005862 
## [536]    train-logloss:0.005831 
## [537]    train-logloss:0.005800 
## [538]    train-logloss:0.005770 
## [539]    train-logloss:0.005740 
## [540]    train-logloss:0.005710 
## [541]    train-logloss:0.005680 
## [542]    train-logloss:0.005650 
## [543]    train-logloss:0.005621 
## [544]    train-logloss:0.005592 
## [545]    train-logloss:0.005563 
## [546]    train-logloss:0.005534 
## [547]    train-logloss:0.005506 
## [548]    train-logloss:0.005478 
## [549]    train-logloss:0.005450 
## [550]    train-logloss:0.005422 
## [551]    train-logloss:0.005395 
## [552]    train-logloss:0.005367 
## [553]    train-logloss:0.005340 
## [554]    train-logloss:0.005313 
## [555]    train-logloss:0.005286 
## [556]    train-logloss:0.005260 
## [557]    train-logloss:0.005234 
## [558]    train-logloss:0.005234 
## [559]    train-logloss:0.005234 
## [560]    train-logloss:0.005234 
## [561]    train-logloss:0.005234 
## [562]    train-logloss:0.005234 
## [563]    train-logloss:0.005234 
## [564]    train-logloss:0.005234 
## [565]    train-logloss:0.005234 
## [566]    train-logloss:0.005234 
## [567]    train-logloss:0.005234 
## [568]    train-logloss:0.005234 
## [569]    train-logloss:0.005234 
## [570]    train-logloss:0.005234 
## [571]    train-logloss:0.005234 
## [572]    train-logloss:0.005234 
## [573]    train-logloss:0.005234 
## [574]    train-logloss:0.005234 
## [575]    train-logloss:0.005234 
## [576]    train-logloss:0.005234 
## [577]    train-logloss:0.005234 
## [578]    train-logloss:0.005234 
## [579]    train-logloss:0.005234 
## [580]    train-logloss:0.005234 
## [581]    train-logloss:0.005234 
## [582]    train-logloss:0.005234 
## [583]    train-logloss:0.005234 
## [584]    train-logloss:0.005234 
## [585]    train-logloss:0.005234 
## [586]    train-logloss:0.005234 
## [587]    train-logloss:0.005234 
## [588]    train-logloss:0.005234 
## [589]    train-logloss:0.005234 
## [590]    train-logloss:0.005234 
## [591]    train-logloss:0.005234 
## [592]    train-logloss:0.005234 
## [593]    train-logloss:0.005234 
## [594]    train-logloss:0.005234 
## [595]    train-logloss:0.005234 
## [596]    train-logloss:0.005234 
## [597]    train-logloss:0.005234 
## [598]    train-logloss:0.005234 
## [599]    train-logloss:0.005234 
## [600]    train-logloss:0.005234 
## [601]    train-logloss:0.005234 
## [602]    train-logloss:0.005234 
## [603]    train-logloss:0.005234 
## [604]    train-logloss:0.005234 
## [605]    train-logloss:0.005234 
## [606]    train-logloss:0.005234 
## [607]    train-logloss:0.005234 
## [608]    train-logloss:0.005234 
## [609]    train-logloss:0.005234 
## [610]    train-logloss:0.005234 
## [611]    train-logloss:0.005234 
## [612]    train-logloss:0.005234 
## [613]    train-logloss:0.005234 
## [614]    train-logloss:0.005234 
## [615]    train-logloss:0.005234 
## [616]    train-logloss:0.005234 
## [617]    train-logloss:0.005234 
## [618]    train-logloss:0.005234 
## [619]    train-logloss:0.005234 
## [620]    train-logloss:0.005234 
## [621]    train-logloss:0.005234 
## [622]    train-logloss:0.005234 
## [623]    train-logloss:0.005234 
## [624]    train-logloss:0.005234 
## [625]    train-logloss:0.005234 
## [626]    train-logloss:0.005234 
## [627]    train-logloss:0.005234 
## [628]    train-logloss:0.005234 
## [629]    train-logloss:0.005234 
## [630]    train-logloss:0.005234 
## [631]    train-logloss:0.005234 
## [632]    train-logloss:0.005234 
## [633]    train-logloss:0.005234 
## [634]    train-logloss:0.005234 
## [635]    train-logloss:0.005234 
## [636]    train-logloss:0.005234 
## [637]    train-logloss:0.005234 
## [638]    train-logloss:0.005234 
## [639]    train-logloss:0.005234 
## [640]    train-logloss:0.005234 
## [641]    train-logloss:0.005234 
## [642]    train-logloss:0.005234 
## [643]    train-logloss:0.005234 
## [644]    train-logloss:0.005234 
## [645]    train-logloss:0.005234 
## [646]    train-logloss:0.005234 
## [647]    train-logloss:0.005234 
## [648]    train-logloss:0.005234 
## [649]    train-logloss:0.005234 
## [650]    train-logloss:0.005234 
## [651]    train-logloss:0.005234 
## [652]    train-logloss:0.005234 
## [653]    train-logloss:0.005234 
## [654]    train-logloss:0.005234 
## [655]    train-logloss:0.005234 
## [656]    train-logloss:0.005234 
## [657]    train-logloss:0.005234 
## [658]    train-logloss:0.005234 
## [659]    train-logloss:0.005234 
## [660]    train-logloss:0.005234 
## [661]    train-logloss:0.005234 
## [662]    train-logloss:0.005234 
## [663]    train-logloss:0.005234 
## [664]    train-logloss:0.005234 
## [665]    train-logloss:0.005234 
## [666]    train-logloss:0.005234 
## [667]    train-logloss:0.005234 
## [668]    train-logloss:0.005234 
## [669]    train-logloss:0.005234 
## [670]    train-logloss:0.005234 
## [671]    train-logloss:0.005234 
## [672]    train-logloss:0.005234 
## [673]    train-logloss:0.005234 
## [674]    train-logloss:0.005234 
## [675]    train-logloss:0.005234 
## [676]    train-logloss:0.005234 
## [677]    train-logloss:0.005234 
## [678]    train-logloss:0.005234 
## [679]    train-logloss:0.005234 
## [680]    train-logloss:0.005234 
## [681]    train-logloss:0.005234 
## [682]    train-logloss:0.005234 
## [683]    train-logloss:0.005234 
## [684]    train-logloss:0.005234 
## [685]    train-logloss:0.005234 
## [686]    train-logloss:0.005234 
## [687]    train-logloss:0.005234 
## [688]    train-logloss:0.005234 
## [689]    train-logloss:0.005234 
## [690]    train-logloss:0.005234 
## [691]    train-logloss:0.005234 
## [692]    train-logloss:0.005234 
## [693]    train-logloss:0.005234 
## [694]    train-logloss:0.005234 
## [695]    train-logloss:0.005234 
## [696]    train-logloss:0.005234 
## [697]    train-logloss:0.005234 
## [698]    train-logloss:0.005234 
## [699]    train-logloss:0.005234 
## [700]    train-logloss:0.005234 
## [701]    train-logloss:0.005234 
## [702]    train-logloss:0.005234 
## [703]    train-logloss:0.005234 
## [704]    train-logloss:0.005234 
## [705]    train-logloss:0.005234 
## [706]    train-logloss:0.005234 
## [707]    train-logloss:0.005234 
## [708]    train-logloss:0.005234 
## [709]    train-logloss:0.005234 
## [710]    train-logloss:0.005234 
## [711]    train-logloss:0.005234 
## [712]    train-logloss:0.005234 
## [713]    train-logloss:0.005234 
## [714]    train-logloss:0.005234 
## [715]    train-logloss:0.005234 
## [716]    train-logloss:0.005234 
## [717]    train-logloss:0.005234 
## [718]    train-logloss:0.005234 
## [719]    train-logloss:0.005234 
## [720]    train-logloss:0.005234 
## [721]    train-logloss:0.005234 
## [722]    train-logloss:0.005234 
## [723]    train-logloss:0.005234 
## [724]    train-logloss:0.005234 
## [725]    train-logloss:0.005234 
## [726]    train-logloss:0.005234 
## [727]    train-logloss:0.005234 
## [728]    train-logloss:0.005234 
## [729]    train-logloss:0.005234 
## [730]    train-logloss:0.005234 
## [731]    train-logloss:0.005234 
## [732]    train-logloss:0.005234 
## [733]    train-logloss:0.005234 
## [734]    train-logloss:0.005234 
## [735]    train-logloss:0.005234 
## [736]    train-logloss:0.005234 
## [737]    train-logloss:0.005234 
## [738]    train-logloss:0.005234 
## [739]    train-logloss:0.005234 
## [740]    train-logloss:0.005234 
## [741]    train-logloss:0.005234 
## [742]    train-logloss:0.005234 
## [743]    train-logloss:0.005234 
## [744]    train-logloss:0.005234 
## [745]    train-logloss:0.005234 
## [746]    train-logloss:0.005234 
## [747]    train-logloss:0.005234 
## [748]    train-logloss:0.005234 
## [749]    train-logloss:0.005234 
## [750]    train-logloss:0.005234 
## [751]    train-logloss:0.005234 
## [752]    train-logloss:0.005234 
## [753]    train-logloss:0.005234 
## [754]    train-logloss:0.005234 
## [755]    train-logloss:0.005234 
## [756]    train-logloss:0.005234 
## [757]    train-logloss:0.005234 
## [758]    train-logloss:0.005234 
## [759]    train-logloss:0.005234 
## [760]    train-logloss:0.005234 
## [761]    train-logloss:0.005234 
## [762]    train-logloss:0.005234 
## [763]    train-logloss:0.005234 
## [764]    train-logloss:0.005234 
## [765]    train-logloss:0.005234 
## [766]    train-logloss:0.005234 
## [767]    train-logloss:0.005234 
## [768]    train-logloss:0.005234 
## [769]    train-logloss:0.005234 
## [770]    train-logloss:0.005234 
## [771]    train-logloss:0.005234 
## [772]    train-logloss:0.005234 
## [773]    train-logloss:0.005234 
## [774]    train-logloss:0.005234 
## [775]    train-logloss:0.005234 
## [776]    train-logloss:0.005234 
## [777]    train-logloss:0.005234 
## [778]    train-logloss:0.005234 
## [779]    train-logloss:0.005234 
## [780]    train-logloss:0.005234 
## [781]    train-logloss:0.005234 
## [782]    train-logloss:0.005234 
## [783]    train-logloss:0.005234 
## [784]    train-logloss:0.005234 
## [785]    train-logloss:0.005234 
## [786]    train-logloss:0.005234 
## [787]    train-logloss:0.005234 
## [788]    train-logloss:0.005234 
## [789]    train-logloss:0.005234 
## [790]    train-logloss:0.005234 
## [791]    train-logloss:0.005234 
## [792]    train-logloss:0.005234 
## [793]    train-logloss:0.005234 
## [794]    train-logloss:0.005234 
## [795]    train-logloss:0.005234 
## [796]    train-logloss:0.005234 
## [797]    train-logloss:0.005234 
## [798]    train-logloss:0.005234 
## [799]    train-logloss:0.005234 
## [800]    train-logloss:0.005234 
## [801]    train-logloss:0.005234 
## [802]    train-logloss:0.005234 
## [803]    train-logloss:0.005234 
## [804]    train-logloss:0.005234 
## [805]    train-logloss:0.005234 
## [806]    train-logloss:0.005234 
## [807]    train-logloss:0.005234 
## [808]    train-logloss:0.005234 
## [809]    train-logloss:0.005234 
## [810]    train-logloss:0.005234 
## [811]    train-logloss:0.005234 
## [812]    train-logloss:0.005234 
## [813]    train-logloss:0.005234 
## [814]    train-logloss:0.005234 
## [815]    train-logloss:0.005234 
## [816]    train-logloss:0.005234 
## [817]    train-logloss:0.005234 
## [818]    train-logloss:0.005234 
## [819]    train-logloss:0.005234 
## [820]    train-logloss:0.005234 
## [821]    train-logloss:0.005234 
## [822]    train-logloss:0.005234 
## [823]    train-logloss:0.005234 
## [824]    train-logloss:0.005234 
## [825]    train-logloss:0.005234 
## [826]    train-logloss:0.005234 
## [827]    train-logloss:0.005234 
## [828]    train-logloss:0.005234 
## [829]    train-logloss:0.005234 
## [830]    train-logloss:0.005234 
## [831]    train-logloss:0.005234 
## [832]    train-logloss:0.005234 
## [833]    train-logloss:0.005234 
## [834]    train-logloss:0.005234 
## [835]    train-logloss:0.005234 
## [836]    train-logloss:0.005234 
## [837]    train-logloss:0.005234 
## [838]    train-logloss:0.005234 
## [839]    train-logloss:0.005234 
## [840]    train-logloss:0.005234 
## [841]    train-logloss:0.005234 
## [842]    train-logloss:0.005234 
## [843]    train-logloss:0.005234 
## [844]    train-logloss:0.005234 
## [845]    train-logloss:0.005234 
## [846]    train-logloss:0.005234 
## [847]    train-logloss:0.005234 
## [848]    train-logloss:0.005234 
## [849]    train-logloss:0.005234 
## [850]    train-logloss:0.005234 
## [851]    train-logloss:0.005234 
## [852]    train-logloss:0.005234 
## [853]    train-logloss:0.005234 
## [854]    train-logloss:0.005234 
## [855]    train-logloss:0.005234 
## [856]    train-logloss:0.005234 
## [857]    train-logloss:0.005234 
## [858]    train-logloss:0.005234 
## [859]    train-logloss:0.005234 
## [860]    train-logloss:0.005234 
## [861]    train-logloss:0.005234 
## [862]    train-logloss:0.005234 
## [863]    train-logloss:0.005234 
## [864]    train-logloss:0.005234 
## [865]    train-logloss:0.005234 
## [866]    train-logloss:0.005234 
## [867]    train-logloss:0.005234 
## [868]    train-logloss:0.005234 
## [869]    train-logloss:0.005234 
## [870]    train-logloss:0.005234 
## [871]    train-logloss:0.005234 
## [872]    train-logloss:0.005234 
## [873]    train-logloss:0.005234 
## [874]    train-logloss:0.005234 
## [875]    train-logloss:0.005234 
## [876]    train-logloss:0.005234 
## [877]    train-logloss:0.005234 
## [878]    train-logloss:0.005234 
## [879]    train-logloss:0.005234 
## [880]    train-logloss:0.005234 
## [881]    train-logloss:0.005234 
## [882]    train-logloss:0.005234 
## [883]    train-logloss:0.005234 
## [884]    train-logloss:0.005234 
## [885]    train-logloss:0.005234 
## [886]    train-logloss:0.005234 
## [887]    train-logloss:0.005234 
## [888]    train-logloss:0.005234 
## [889]    train-logloss:0.005234 
## [890]    train-logloss:0.005234 
## [891]    train-logloss:0.005234 
## [892]    train-logloss:0.005234 
## [893]    train-logloss:0.005234 
## [894]    train-logloss:0.005234 
## [895]    train-logloss:0.005234 
## [896]    train-logloss:0.005234 
## [897]    train-logloss:0.005234 
## [898]    train-logloss:0.005234 
## [899]    train-logloss:0.005234 
## [900]    train-logloss:0.005234 
## [901]    train-logloss:0.005234 
## [902]    train-logloss:0.005234 
## [903]    train-logloss:0.005234 
## [904]    train-logloss:0.005234 
## [905]    train-logloss:0.005234 
## [906]    train-logloss:0.005234 
## [907]    train-logloss:0.005234 
## [908]    train-logloss:0.005234 
## [909]    train-logloss:0.005234 
## [910]    train-logloss:0.005234 
## [911]    train-logloss:0.005234 
## [912]    train-logloss:0.005234 
## [913]    train-logloss:0.005234 
## [914]    train-logloss:0.005234 
## [915]    train-logloss:0.005234 
## [916]    train-logloss:0.005234 
## [917]    train-logloss:0.005234 
## [918]    train-logloss:0.005234 
## [919]    train-logloss:0.005234 
## [920]    train-logloss:0.005234 
## [921]    train-logloss:0.005234 
## [922]    train-logloss:0.005234 
## [923]    train-logloss:0.005234 
## [924]    train-logloss:0.005234 
## [925]    train-logloss:0.005234 
## [926]    train-logloss:0.005234 
## [927]    train-logloss:0.005234 
## [928]    train-logloss:0.005234 
## [929]    train-logloss:0.005234 
## [930]    train-logloss:0.005234 
## [931]    train-logloss:0.005234 
## [932]    train-logloss:0.005234 
## [933]    train-logloss:0.005234 
## [934]    train-logloss:0.005234 
## [935]    train-logloss:0.005234 
## [936]    train-logloss:0.005234 
## [937]    train-logloss:0.005234 
## [938]    train-logloss:0.005234 
## [939]    train-logloss:0.005234 
## [940]    train-logloss:0.005234 
## [941]    train-logloss:0.005234 
## [942]    train-logloss:0.005234 
## [943]    train-logloss:0.005234 
## [944]    train-logloss:0.005234 
## [945]    train-logloss:0.005234 
## [946]    train-logloss:0.005234 
## [947]    train-logloss:0.005234 
## [948]    train-logloss:0.005234 
## [949]    train-logloss:0.005234 
## [950]    train-logloss:0.005234 
## [951]    train-logloss:0.005234 
## [952]    train-logloss:0.005234 
## [953]    train-logloss:0.005234 
## [954]    train-logloss:0.005234 
## [955]    train-logloss:0.005234 
## [956]    train-logloss:0.005234 
## [957]    train-logloss:0.005234 
## [958]    train-logloss:0.005234 
## [959]    train-logloss:0.005234 
## [960]    train-logloss:0.005234 
## [961]    train-logloss:0.005234 
## [962]    train-logloss:0.005234 
## [963]    train-logloss:0.005234 
## [964]    train-logloss:0.005234 
## [965]    train-logloss:0.005234 
## [966]    train-logloss:0.005234 
## [967]    train-logloss:0.005234 
## [968]    train-logloss:0.005234 
## [969]    train-logloss:0.005234 
## [970]    train-logloss:0.005234 
## [971]    train-logloss:0.005234 
## [972]    train-logloss:0.005234 
## [973]    train-logloss:0.005234 
## [974]    train-logloss:0.005234 
## [975]    train-logloss:0.005234 
## [976]    train-logloss:0.005234 
## [977]    train-logloss:0.005234 
## [978]    train-logloss:0.005234 
## [979]    train-logloss:0.005234 
## [980]    train-logloss:0.005234 
## [981]    train-logloss:0.005234 
## [982]    train-logloss:0.005234 
## [983]    train-logloss:0.005234 
## [984]    train-logloss:0.005234 
## [985]    train-logloss:0.005234 
## [986]    train-logloss:0.005234 
## [987]    train-logloss:0.005234 
## [988]    train-logloss:0.005234 
## [989]    train-logloss:0.005234 
## [990]    train-logloss:0.005234 
## [991]    train-logloss:0.005234 
## [992]    train-logloss:0.005234 
## [993]    train-logloss:0.005234 
## [994]    train-logloss:0.005234 
## [995]    train-logloss:0.005234 
## [996]    train-logloss:0.005234 
## [997]    train-logloss:0.005234 
## [998]    train-logloss:0.005234 
## [999]    train-logloss:0.005234 
## [1000]   train-logloss:0.005234

SMOTE

library(xgboost)
smote.train.matrix<-data.matrix(smote_train[,-10])
promo3<-as.matrix(as.factor(as.character(smote_train$promo)))
xgbModel3 <- xgboost(data = smote.train.matrix, 
                    label = promo3,
                    nrounds = 1000,
                    max_depth = 2,
                    eta = 0.01,
                    objective = "binary:logistic")

## [1]  train-logloss:0.690731 
## [2]  train-logloss:0.688362 
## [3]  train-logloss:0.686039 
## [4]  train-logloss:0.683759 
## [5]  train-logloss:0.681523 
## [6]  train-logloss:0.679330 
## [7]  train-logloss:0.677178 
## [8]  train-logloss:0.675066 
## [9]  train-logloss:0.672994 
## [10] train-logloss:0.670960 
## [11] train-logloss:0.668962 
## [12] train-logloss:0.667000 
## [13] train-logloss:0.665071 
## [14] train-logloss:0.663176 
## [15] train-logloss:0.661314 
## [16] train-logloss:0.659484 
## [17] train-logloss:0.657686 
## [18] train-logloss:0.655919 
## [19] train-logloss:0.654182 
## [20] train-logloss:0.652475 
## [21] train-logloss:0.650797 
## [22] train-logloss:0.649147 
## [23] train-logloss:0.647526 
## [24] train-logloss:0.645931 
## [25] train-logloss:0.644364 
## [26] train-logloss:0.642823 
## [27] train-logloss:0.641308 
## [28] train-logloss:0.639818 
## [29] train-logloss:0.638353 
## [30] train-logloss:0.636912 
## [31] train-logloss:0.635496 
## [32] train-logloss:0.634103 
## [33] train-logloss:0.632733 
## [34] train-logloss:0.631571 
## [35] train-logloss:0.630430 
## [36] train-logloss:0.629310 
## [37] train-logloss:0.628003 
## [38] train-logloss:0.626918 
## [39] train-logloss:0.625852 
## [40] train-logloss:0.624805 
## [41] train-logloss:0.623778 
## [42] train-logloss:0.622544 
## [43] train-logloss:0.621548 
## [44] train-logloss:0.620569 
## [45] train-logloss:0.619609 
## [46] train-logloss:0.618665 
## [47] train-logloss:0.617500 
## [48] train-logloss:0.616585 
## [49] train-logloss:0.615686 
## [50] train-logloss:0.614712 
## [51] train-logloss:0.613840 
## [52] train-logloss:0.612893 
## [53] train-logloss:0.612047 
## [54] train-logloss:0.611125 
## [55] train-logloss:0.610304 
## [56] train-logloss:0.609456 
## [57] train-logloss:0.608567 
## [58] train-logloss:0.607743 
## [59] train-logloss:0.606706 
## [60] train-logloss:0.605933 
## [61] train-logloss:0.605137 
## [62] train-logloss:0.604385 
## [63] train-logloss:0.603609 
## [64] train-logloss:0.602848 
## [65] train-logloss:0.601863 
## [66] train-logloss:0.601145 
## [67] train-logloss:0.600406 
## [68] train-logloss:0.599707 
## [69] train-logloss:0.599021 
## [70] train-logloss:0.598306 
## [71] train-logloss:0.597530 
## [72] train-logloss:0.596958 
## [73] train-logloss:0.596358 
## [74] train-logloss:0.595704 
## [75] train-logloss:0.595149 
## [76] train-logloss:0.594564 
## [77] train-logloss:0.593880 
## [78] train-logloss:0.592979 
## [79] train-logloss:0.592407 
## [80] train-logloss:0.591784 
## [81] train-logloss:0.591254 
## [82] train-logloss:0.590697 
## [83] train-logloss:0.590178 
## [84] train-logloss:0.589304 
## [85] train-logloss:0.588759 
## [86] train-logloss:0.588163 
## [87] train-logloss:0.587661 
## [88] train-logloss:0.587128 
## [89] train-logloss:0.586494 
## [90] train-logloss:0.585916 
## [91] train-logloss:0.585431 
## [92] train-logloss:0.584913 
## [93] train-logloss:0.584078 
## [94] train-logloss:0.583521 
## [95] train-logloss:0.583013 
## [96] train-logloss:0.582544 
## [97] train-logloss:0.582083 
## [98] train-logloss:0.581587 
## [99] train-logloss:0.580776 
## [100]    train-logloss:0.580242 
## [101]    train-logloss:0.579611 
## [102]    train-logloss:0.579126 
## [103]    train-logloss:0.578606 
## [104]    train-logloss:0.578166 
## [105]    train-logloss:0.577552 
## [106]    train-logloss:0.577080 
## [107]    train-logloss:0.576650 
## [108]    train-logloss:0.576049 
## [109]    train-logloss:0.575553 
## [110]    train-logloss:0.575091 
## [111]    train-logloss:0.574504 
## [112]    train-logloss:0.574020 
## [113]    train-logloss:0.573569 
## [114]    train-logloss:0.573158 
## [115]    train-logloss:0.572586 
## [116]    train-logloss:0.572145 
## [117]    train-logloss:0.571676 
## [118]    train-logloss:0.571278 
## [119]    train-logloss:0.570721 
## [120]    train-logloss:0.570289 
## [121]    train-logloss:0.569900 
## [122]    train-logloss:0.569354 
## [123]    train-logloss:0.568904 
## [124]    train-logloss:0.568483 
## [125]    train-logloss:0.567949 
## [126]    train-logloss:0.567573 
## [127]    train-logloss:0.567049 
## [128]    train-logloss:0.566615 
## [129]    train-logloss:0.566204 
## [130]    train-logloss:0.565839 
## [131]    train-logloss:0.565327 
## [132]    train-logloss:0.564925 
## [133]    train-logloss:0.564505 
## [134]    train-logloss:0.564004 
## [135]    train-logloss:0.563610 
## [136]    train-logloss:0.563258 
## [137]    train-logloss:0.562850 
## [138]    train-logloss:0.562361 
## [139]    train-logloss:0.561976 
## [140]    train-logloss:0.561633 
## [141]    train-logloss:0.561154 
## [142]    train-logloss:0.560760 
## [143]    train-logloss:0.560383 
## [144]    train-logloss:0.560050 
## [145]    train-logloss:0.559582 
## [146]    train-logloss:0.559213 
## [147]    train-logloss:0.558831 
## [148]    train-logloss:0.558372 
## [149]    train-logloss:0.558049 
## [150]    train-logloss:0.557687 
## [151]    train-logloss:0.557314 
## [152]    train-logloss:0.556865 
## [153]    train-logloss:0.556551 
## [154]    train-logloss:0.556197 
## [155]    train-logloss:0.555755 
## [156]    train-logloss:0.555394 
## [157]    train-logloss:0.555086 
## [158]    train-logloss:0.554617 
## [159]    train-logloss:0.554183 
## [160]    train-logloss:0.553828 
## [161]    train-logloss:0.553485 
## [162]    train-logloss:0.553185 
## [163]    train-logloss:0.552835 
## [164]    train-logloss:0.552377 
## [165]    train-logloss:0.551953 
## [166]    train-logloss:0.551658 
## [167]    train-logloss:0.551207 
## [168]    train-logloss:0.550789 
## [169]    train-logloss:0.550448 
## [170]    train-logloss:0.550004 
## [171]    train-logloss:0.549716 
## [172]    train-logloss:0.549306 
## [173]    train-logloss:0.548981 
## [174]    train-logloss:0.548648 
## [175]    train-logloss:0.548365 
## [176]    train-logloss:0.547963 
## [177]    train-logloss:0.547528 
## [178]    train-logloss:0.547201 
## [179]    train-logloss:0.546925 
## [180]    train-logloss:0.546530 
## [181]    train-logloss:0.546103 
## [182]    train-logloss:0.545782 
## [183]    train-logloss:0.545470 
## [184]    train-logloss:0.545082 
## [185]    train-logloss:0.544767 
## [186]    train-logloss:0.544347 
## [187]    train-logloss:0.544078 
## [188]    train-logloss:0.543664 
## [189]    train-logloss:0.543283 
## [190]    train-logloss:0.543020 
## [191]    train-logloss:0.542717 
## [192]    train-logloss:0.542343 
## [193]    train-logloss:0.542038 
## [194]    train-logloss:0.541631 
## [195]    train-logloss:0.541374 
## [196]    train-logloss:0.541004 
## [197]    train-logloss:0.540703 
## [198]    train-logloss:0.540304 
## [199]    train-logloss:0.540051 
## [200]    train-logloss:0.539656 
## [201]    train-logloss:0.539407 
## [202]    train-logloss:0.539045 
## [203]    train-logloss:0.538756 
## [204]    train-logloss:0.538463 
## [205]    train-logloss:0.538109 
## [206]    train-logloss:0.537719 
## [207]    train-logloss:0.537474 
## [208]    train-logloss:0.537187 
## [209]    train-logloss:0.536806 
## [210]    train-logloss:0.536566 
## [211]    train-logloss:0.536216 
## [212]    train-logloss:0.535934 
## [213]    train-logloss:0.535559 
## [214]    train-logloss:0.535214 
## [215]    train-logloss:0.534938 
## [216]    train-logloss:0.534702 
## [217]    train-logloss:0.534364 
## [218]    train-logloss:0.533995 
## [219]    train-logloss:0.533718 
## [220]    train-logloss:0.533487 
## [221]    train-logloss:0.533215 
## [222]    train-logloss:0.532852 
## [223]    train-logloss:0.532520 
## [224]    train-logloss:0.532293 
## [225]    train-logloss:0.532025 
## [226]    train-logloss:0.531698 
## [227]    train-logloss:0.531341 
## [228]    train-logloss:0.531076 
## [229]    train-logloss:0.530852 
## [230]    train-logloss:0.530502 
## [231]    train-logloss:0.530179 
## [232]    train-logloss:0.529918 
## [233]    train-logloss:0.529699 
## [234]    train-logloss:0.529353 
## [235]    train-logloss:0.529036 
## [236]    train-logloss:0.528820 
## [237]    train-logloss:0.528564 
## [238]    train-logloss:0.528308 
## [239]    train-logloss:0.527997 
## [240]    train-logloss:0.527657 
## [241]    train-logloss:0.527445 
## [242]    train-logloss:0.527109 
## [243]    train-logloss:0.526617 
## [244]    train-logloss:0.526311 
## [245]    train-logloss:0.526104 
## [246]    train-logloss:0.525856 
## [247]    train-logloss:0.525608 
## [248]    train-logloss:0.525280 
## [249]    train-logloss:0.524979 
## [250]    train-logloss:0.524776 
## [251]    train-logloss:0.524533 
## [252]    train-logloss:0.524210 
## [253]    train-logloss:0.523738 
## [254]    train-logloss:0.523443 
## [255]    train-logloss:0.523126 
## [256]    train-logloss:0.522928 
## [257]    train-logloss:0.522688 
## [258]    train-logloss:0.522397 
## [259]    train-logloss:0.522162 
## [260]    train-logloss:0.521849 
## [261]    train-logloss:0.521655 
## [262]    train-logloss:0.521369 
## [263]    train-logloss:0.521060 
## [264]    train-logloss:0.520870 
## [265]    train-logloss:0.520418 
## [266]    train-logloss:0.520138 
## [267]    train-logloss:0.519834 
## [268]    train-logloss:0.519648 
## [269]    train-logloss:0.519418 
## [270]    train-logloss:0.519141 
## [271]    train-logloss:0.518914 
## [272]    train-logloss:0.518615 
## [273]    train-logloss:0.518432 
## [274]    train-logloss:0.517998 
## [275]    train-logloss:0.517726 
## [276]    train-logloss:0.517434 
## [277]    train-logloss:0.517212 
## [278]    train-logloss:0.517033 
## [279]    train-logloss:0.516810 
## [280]    train-logloss:0.516543 
## [281]    train-logloss:0.516255 
## [282]    train-logloss:0.516079 
## [283]    train-logloss:0.515660 
## [284]    train-logloss:0.515398 
## [285]    train-logloss:0.515115 
## [286]    train-logloss:0.514943 
## [287]    train-logloss:0.514684 
## [288]    train-logloss:0.514471 
## [289]    train-logloss:0.514191 
## [290]    train-logloss:0.514023 
## [291]    train-logloss:0.513619 
## [292]    train-logloss:0.513403 
## [293]    train-logloss:0.513149 
## [294]    train-logloss:0.512941 
## [295]    train-logloss:0.512668 
## [296]    train-logloss:0.512502 
## [297]    train-logloss:0.512233 
## [298]    train-logloss:0.511841 
## [299]    train-logloss:0.511591 
## [300]    train-logloss:0.511386 
## [301]    train-logloss:0.511176 
## [302]    train-logloss:0.510930 
## [303]    train-logloss:0.510768 
## [304]    train-logloss:0.510505 
## [305]    train-logloss:0.510122 
## [306]    train-logloss:0.509964 
## [307]    train-logloss:0.509721 
## [308]    train-logloss:0.509463 
## [309]    train-logloss:0.509087 
## [310]    train-logloss:0.508884 
## [311]    train-logloss:0.508643 
## [312]    train-logloss:0.508444 
## [313]    train-logloss:0.508290 
## [314]    train-logloss:0.508037 
## [315]    train-logloss:0.507671 
## [316]    train-logloss:0.507434 
## [317]    train-logloss:0.507282 
## [318]    train-logloss:0.507034 
## [319]    train-logloss:0.506675 
## [320]    train-logloss:0.506477 
## [321]    train-logloss:0.506244 
## [322]    train-logloss:0.506095 
## [323]    train-logloss:0.505742 
## [324]    train-logloss:0.505500 
## [325]    train-logloss:0.505270 
## [326]    train-logloss:0.504923 
## [327]    train-logloss:0.504730 
## [328]    train-logloss:0.504538 
## [329]    train-logloss:0.504311 
## [330]    train-logloss:0.504166 
## [331]    train-logloss:0.503931 
## [332]    train-logloss:0.503591 
## [333]    train-logloss:0.503449 
## [334]    train-logloss:0.503216 
## [335]    train-logloss:0.502884 
## [336]    train-logloss:0.502660 
## [337]    train-logloss:0.502473 
## [338]    train-logloss:0.502144 
## [339]    train-logloss:0.501924 
## [340]    train-logloss:0.501784 
## [341]    train-logloss:0.501559 
## [342]    train-logloss:0.501237 
## [343]    train-logloss:0.501019 
## [344]    train-logloss:0.500882 
## [345]    train-logloss:0.500660 
## [346]    train-logloss:0.500524 
## [347]    train-logloss:0.500309 
## [348]    train-logloss:0.499995 
## [349]    train-logloss:0.499813 
## [350]    train-logloss:0.499679 
## [351]    train-logloss:0.499462 
## [352]    train-logloss:0.499250 
## [353]    train-logloss:0.498941 
## [354]    train-logloss:0.498763 
## [355]    train-logloss:0.498632 
## [356]    train-logloss:0.498423 
## [357]    train-logloss:0.498211 
## [358]    train-logloss:0.497909 
## [359]    train-logloss:0.497701 
## [360]    train-logloss:0.497572 
## [361]    train-logloss:0.497364 
## [362]    train-logloss:0.497068 
## [363]    train-logloss:0.496895 
## [364]    train-logloss:0.496691 
## [365]    train-logloss:0.496564 
## [366]    train-logloss:0.496272 
## [367]    train-logloss:0.496068 
## [368]    train-logloss:0.495867 
## [369]    train-logloss:0.495581 
## [370]    train-logloss:0.495456 
## [371]    train-logloss:0.495287 
## [372]    train-logloss:0.495088 
## [373]    train-logloss:0.494965 
## [374]    train-logloss:0.494843 
## [375]    train-logloss:0.494562 
## [376]    train-logloss:0.494364 
## [377]    train-logloss:0.494169 
## [378]    train-logloss:0.493892 
## [379]    train-logloss:0.493726 
## [380]    train-logloss:0.493533 
## [381]    train-logloss:0.493413 
## [382]    train-logloss:0.493219 
## [383]    train-logloss:0.492948 
## [384]    train-logloss:0.492830 
## [385]    train-logloss:0.492639 
## [386]    train-logloss:0.492477 
## [387]    train-logloss:0.492361 
## [388]    train-logloss:0.492173 
## [389]    train-logloss:0.492058 
## [390]    train-logloss:0.491867 
## [391]    train-logloss:0.491602 
## [392]    train-logloss:0.491416 
## [393]    train-logloss:0.491302 
## [394]    train-logloss:0.491115 
## [395]    train-logloss:0.490855 
## [396]    train-logloss:0.490672 
## [397]    train-logloss:0.490514 
## [398]    train-logloss:0.490402 
## [399]    train-logloss:0.490221 
## [400]    train-logloss:0.489965 
## [401]    train-logloss:0.489783 
## [402]    train-logloss:0.489532 
## [403]    train-logloss:0.489353 
## [404]    train-logloss:0.489198 
## [405]    train-logloss:0.489088 
## [406]    train-logloss:0.488840 
## [407]    train-logloss:0.488662 
## [408]    train-logloss:0.488554 
## [409]    train-logloss:0.488377 
## [410]    train-logloss:0.488225 
## [411]    train-logloss:0.488050 
## [412]    train-logloss:0.487942 
## [413]    train-logloss:0.487836 
## [414]    train-logloss:0.487663 
## [415]    train-logloss:0.487421 
## [416]    train-logloss:0.487247 
## [417]    train-logloss:0.487009 
## [418]    train-logloss:0.486904 
## [419]    train-logloss:0.486733 
## [420]    train-logloss:0.486585 
## [421]    train-logloss:0.486481 
## [422]    train-logloss:0.486247 
## [423]    train-logloss:0.486079 
## [424]    train-logloss:0.485976 
## [425]    train-logloss:0.485813 
## [426]    train-logloss:0.485668 
## [427]    train-logloss:0.485567 
## [428]    train-logloss:0.485400 
## [429]    train-logloss:0.485171 
## [430]    train-logloss:0.485004 
## [431]    train-logloss:0.484778 
## [432]    train-logloss:0.484613 
## [433]    train-logloss:0.484513 
## [434]    train-logloss:0.484290 
## [435]    train-logloss:0.484148 
## [436]    train-logloss:0.484049 
## [437]    train-logloss:0.483887 
## [438]    train-logloss:0.483722 
## [439]    train-logloss:0.483625 
## [440]    train-logloss:0.483465 
## [441]    train-logloss:0.483246 
## [442]    train-logloss:0.483106 
## [443]    train-logloss:0.483010 
## [444]    train-logloss:0.482852 
## [445]    train-logloss:0.482690 
## [446]    train-logloss:0.482532 
## [447]    train-logloss:0.482437 
## [448]    train-logloss:0.482282 
## [449]    train-logloss:0.482122 
## [450]    train-logloss:0.481906 
## [451]    train-logloss:0.481750 
## [452]    train-logloss:0.481657 
## [453]    train-logloss:0.481503 
## [454]    train-logloss:0.481367 
## [455]    train-logloss:0.481212 
## [456]    train-logloss:0.481119 
## [457]    train-logloss:0.480907 
## [458]    train-logloss:0.480748 
## [459]    train-logloss:0.480656 
## [460]    train-logloss:0.480505 
## [461]    train-logloss:0.480352 
## [462]    train-logloss:0.480218 
## [463]    train-logloss:0.480127 
## [464]    train-logloss:0.479977 
## [465]    train-logloss:0.479820 
## [466]    train-logloss:0.479670 
## [467]    train-logloss:0.479521 
## [468]    train-logloss:0.479312 
## [469]    train-logloss:0.479157 
## [470]    train-logloss:0.479066 
## [471]    train-logloss:0.478916 
## [472]    train-logloss:0.478770 
## [473]    train-logloss:0.478680 
## [474]    train-logloss:0.478474 
## [475]    train-logloss:0.478343 
## [476]    train-logloss:0.478195 
## [477]    train-logloss:0.478050 
## [478]    train-logloss:0.477896 
## [479]    train-logloss:0.477806 
## [480]    train-logloss:0.477655 
## [481]    train-logloss:0.477566 
## [482]    train-logloss:0.477422 
## [483]    train-logloss:0.477276 
## [484]    train-logloss:0.477073 
## [485]    train-logloss:0.476945 
## [486]    train-logloss:0.476803 
## [487]    train-logloss:0.476615 
## [488]    train-logloss:0.476527 
## [489]    train-logloss:0.476383 
## [490]    train-logloss:0.476182 
## [491]    train-logloss:0.476032 
## [492]    train-logloss:0.475847 
## [493]    train-logloss:0.475706 
## [494]    train-logloss:0.475523 
## [495]    train-logloss:0.475375 
## [496]    train-logloss:0.475288 
## [497]    train-logloss:0.475148 
## [498]    train-logloss:0.474968 
## [499]    train-logloss:0.474821 
## [500]    train-logloss:0.474734 
## [501]    train-logloss:0.474592 
## [502]    train-logloss:0.474453 
## [503]    train-logloss:0.474255 
## [504]    train-logloss:0.474131 
## [505]    train-logloss:0.474045 
## [506]    train-logloss:0.473905 
## [507]    train-logloss:0.473709 
## [508]    train-logloss:0.473571 
## [509]    train-logloss:0.473427 
## [510]    train-logloss:0.473249 
## [511]    train-logloss:0.473164 
## [512]    train-logloss:0.473028 
## [513]    train-logloss:0.472853 
## [514]    train-logloss:0.472713 
## [515]    train-logloss:0.472592 
## [516]    train-logloss:0.472419 
## [517]    train-logloss:0.472283 
## [518]    train-logloss:0.472199 
## [519]    train-logloss:0.472028 
## [520]    train-logloss:0.471891 
## [521]    train-logloss:0.471697 
## [522]    train-logloss:0.471555 
## [523]    train-logloss:0.471471 
## [524]    train-logloss:0.471337 
## [525]    train-logloss:0.471198 
## [526]    train-logloss:0.471007 
## [527]    train-logloss:0.470924 
## [528]    train-logloss:0.470787 
## [529]    train-logloss:0.470598 
## [530]    train-logloss:0.470479 
## [531]    train-logloss:0.470346 
## [532]    train-logloss:0.470177 
## [533]    train-logloss:0.470040 
## [534]    train-logloss:0.469958 
## [535]    train-logloss:0.469772 
## [536]    train-logloss:0.469640 
## [537]    train-logloss:0.469521 
## [538]    train-logloss:0.469387 
## [539]    train-logloss:0.469305 
## [540]    train-logloss:0.469139 
## [541]    train-logloss:0.469008 
## [542]    train-logloss:0.468872 
## [543]    train-logloss:0.468708 
## [544]    train-logloss:0.468547 
## [545]    train-logloss:0.468415 
## [546]    train-logloss:0.468285 
## [547]    train-logloss:0.468126 
## [548]    train-logloss:0.468044 
## [549]    train-logloss:0.467888 
## [550]    train-logloss:0.467759 
## [551]    train-logloss:0.467623 
## [552]    train-logloss:0.467543 
## [553]    train-logloss:0.467358 
## [554]    train-logloss:0.467225 
## [555]    train-logloss:0.467004 
## [556]    train-logloss:0.466926 
## [557]    train-logloss:0.466798 
## [558]    train-logloss:0.466683 
## [559]    train-logloss:0.466528 
## [560]    train-logloss:0.466450 
## [561]    train-logloss:0.466267 
## [562]    train-logloss:0.466137 
## [563]    train-logloss:0.466010 
## [564]    train-logloss:0.465857 
## [565]    train-logloss:0.465731 
## [566]    train-logloss:0.465654 
## [567]    train-logloss:0.465541 
## [568]    train-logloss:0.465390 
## [569]    train-logloss:0.465261 
## [570]    train-logloss:0.465046 
## [571]    train-logloss:0.464969 
## [572]    train-logloss:0.464845 
## [573]    train-logloss:0.464696 
## [574]    train-logloss:0.464484 
## [575]    train-logloss:0.464408 
## [576]    train-logloss:0.464261 
## [577]    train-logloss:0.464138 
## [578]    train-logloss:0.463993 
## [579]    train-logloss:0.463918 
## [580]    train-logloss:0.463797 
## [581]    train-logloss:0.463653 
## [582]    train-logloss:0.463526 
## [583]    train-logloss:0.463343 
## [584]    train-logloss:0.463213 
## [585]    train-logloss:0.463005 
## [586]    train-logloss:0.462932 
## [587]    train-logloss:0.462752 
## [588]    train-logloss:0.462631 
## [589]    train-logloss:0.462427 
## [590]    train-logloss:0.462354 
## [591]    train-logloss:0.462153 
## [592]    train-logloss:0.462082 
## [593]    train-logloss:0.461962 
## [594]    train-logloss:0.461820 
## [595]    train-logloss:0.461749 
## [596]    train-logloss:0.461631 
## [597]    train-logloss:0.461490 
## [598]    train-logloss:0.461293 
## [599]    train-logloss:0.461223 
## [600]    train-logloss:0.461084 
## [601]    train-logloss:0.461003 
## [602]    train-logloss:0.460886 
## [603]    train-logloss:0.460750 
## [604]    train-logloss:0.460680 
## [605]    train-logloss:0.460600 
## [606]    train-logloss:0.460465 
## [607]    train-logloss:0.460349 
## [608]    train-logloss:0.460216 
## [609]    train-logloss:0.460138 
## [610]    train-logloss:0.460023 
## [611]    train-logloss:0.459953 
## [612]    train-logloss:0.459822 
## [613]    train-logloss:0.459745 
## [614]    train-logloss:0.459615 
## [615]    train-logloss:0.459501 
## [616]    train-logloss:0.459391 
## [617]    train-logloss:0.459322 
## [618]    train-logloss:0.459194 
## [619]    train-logloss:0.459002 
## [620]    train-logloss:0.458926 
## [621]    train-logloss:0.458800 
## [622]    train-logloss:0.458687 
## [623]    train-logloss:0.458618 
## [624]    train-logloss:0.458543 
## [625]    train-logloss:0.458419 
## [626]    train-logloss:0.458306 
## [627]    train-logloss:0.458178 
## [628]    train-logloss:0.458110 
## [629]    train-logloss:0.457999 
## [630]    train-logloss:0.457925 
## [631]    train-logloss:0.457798 
## [632]    train-logloss:0.457730 
## [633]    train-logloss:0.457604 
## [634]    train-logloss:0.457495 
## [635]    train-logloss:0.457307 
## [636]    train-logloss:0.457240 
## [637]    train-logloss:0.457167 
## [638]    train-logloss:0.457043 
## [639]    train-logloss:0.456934 
## [640]    train-logloss:0.456812 
## [641]    train-logloss:0.456746 
## [642]    train-logloss:0.456673 
## [643]    train-logloss:0.456565 
## [644]    train-logloss:0.456457 
## [645]    train-logloss:0.456337 
## [646]    train-logloss:0.456265 
## [647]    train-logloss:0.456147 
## [648]    train-logloss:0.456080 
## [649]    train-logloss:0.455972 
## [650]    train-logloss:0.455848 
## [651]    train-logloss:0.455777 
## [652]    train-logloss:0.455671 
## [653]    train-logloss:0.455554 
## [654]    train-logloss:0.455488 
## [655]    train-logloss:0.455381 
## [656]    train-logloss:0.455258 
## [657]    train-logloss:0.455188 
## [658]    train-logloss:0.455065 
## [659]    train-logloss:0.454881 
## [660]    train-logloss:0.454816 
## [661]    train-logloss:0.454711 
## [662]    train-logloss:0.454530 
## [663]    train-logloss:0.454466 
## [664]    train-logloss:0.454287 
## [665]    train-logloss:0.454224 
## [666]    train-logloss:0.454119 
## [667]    train-logloss:0.453946 
## [668]    train-logloss:0.453770 
## [669]    train-logloss:0.453707 
## [670]    train-logloss:0.453638 
## [671]    train-logloss:0.453521 
## [672]    train-logloss:0.453416 
## [673]    train-logloss:0.453354 
## [674]    train-logloss:0.453286 
## [675]    train-logloss:0.453170 
## [676]    train-logloss:0.453066 
## [677]    train-logloss:0.452999 
## [678]    train-logloss:0.452885 
## [679]    train-logloss:0.452782 
## [680]    train-logloss:0.452600 
## [681]    train-logloss:0.452479 
## [682]    train-logloss:0.452360 
## [683]    train-logloss:0.452298 
## [684]    train-logloss:0.452126 
## [685]    train-logloss:0.452023 
## [686]    train-logloss:0.451962 
## [687]    train-logloss:0.451791 
## [688]    train-logloss:0.451725 
## [689]    train-logloss:0.451664 
## [690]    train-logloss:0.451562 
## [691]    train-logloss:0.451496 
## [692]    train-logloss:0.451383 
## [693]    train-logloss:0.451263 
## [694]    train-logloss:0.451162 
## [695]    train-logloss:0.451101 
## [696]    train-logloss:0.451000 
## [697]    train-logloss:0.450935 
## [698]    train-logloss:0.450824 
## [699]    train-logloss:0.450723 
## [700]    train-logloss:0.450612 
## [701]    train-logloss:0.450437 
## [702]    train-logloss:0.450322 
## [703]    train-logloss:0.450262 
## [704]    train-logloss:0.450198 
## [705]    train-logloss:0.450098 
## [706]    train-logloss:0.449980 
## [707]    train-logloss:0.449921 
## [708]    train-logloss:0.449754 
## [709]    train-logloss:0.449691 
## [710]    train-logloss:0.449520 
## [711]    train-logloss:0.449422 
## [712]    train-logloss:0.449322 
## [713]    train-logloss:0.449264 
## [714]    train-logloss:0.449154 
## [715]    train-logloss:0.449092 
## [716]    train-logloss:0.448925 
## [717]    train-logloss:0.448809 
## [718]    train-logloss:0.448710 
## [719]    train-logloss:0.448597 
## [720]    train-logloss:0.448539 
## [721]    train-logloss:0.448376 
## [722]    train-logloss:0.448315 
## [723]    train-logloss:0.448217 
## [724]    train-logloss:0.448108 
## [725]    train-logloss:0.447945 
## [726]    train-logloss:0.447885 
## [727]    train-logloss:0.447724 
## [728]    train-logloss:0.447610 
## [729]    train-logloss:0.447512 
## [730]    train-logloss:0.447386 
## [731]    train-logloss:0.447328 
## [732]    train-logloss:0.447269 
## [733]    train-logloss:0.447172 
## [734]    train-logloss:0.447062 
## [735]    train-logloss:0.447005 
## [736]    train-logloss:0.446846 
## [737]    train-logloss:0.446789 
## [738]    train-logloss:0.446632 
## [739]    train-logloss:0.446574 
## [740]    train-logloss:0.446477 
## [741]    train-logloss:0.446421 
## [742]    train-logloss:0.446314 
## [743]    train-logloss:0.446256 
## [744]    train-logloss:0.446100 
## [745]    train-logloss:0.446004 
## [746]    train-logloss:0.445899 
## [747]    train-logloss:0.445776 
## [748]    train-logloss:0.445720 
## [749]    train-logloss:0.445607 
## [750]    train-logloss:0.445550 
## [751]    train-logloss:0.445396 
## [752]    train-logloss:0.445300 
## [753]    train-logloss:0.445180 
## [754]    train-logloss:0.445124 
## [755]    train-logloss:0.444970 
## [756]    train-logloss:0.444914 
## [757]    train-logloss:0.444763 
## [758]    train-logloss:0.444667 
## [759]    train-logloss:0.444564 
## [760]    train-logloss:0.444415 
## [761]    train-logloss:0.444302 
## [762]    train-logloss:0.444247 
## [763]    train-logloss:0.444193 
## [764]    train-logloss:0.444098 
## [765]    train-logloss:0.443993 
## [766]    train-logloss:0.443939 
## [767]    train-logloss:0.443884 
## [768]    train-logloss:0.443790 
## [769]    train-logloss:0.443672 
## [770]    train-logloss:0.443561 
## [771]    train-logloss:0.443507 
## [772]    train-logloss:0.443414 
## [773]    train-logloss:0.443321 
## [774]    train-logloss:0.443205 
## [775]    train-logloss:0.443151 
## [776]    train-logloss:0.443098 
## [777]    train-logloss:0.442948 
## [778]    train-logloss:0.442894 
## [779]    train-logloss:0.442747 
## [780]    train-logloss:0.442646 
## [781]    train-logloss:0.442502 
## [782]    train-logloss:0.442449 
## [783]    train-logloss:0.442357 
## [784]    train-logloss:0.442254 
## [785]    train-logloss:0.442201 
## [786]    train-logloss:0.442110 
## [787]    train-logloss:0.442000 
## [788]    train-logloss:0.441888 
## [789]    train-logloss:0.441835 
## [790]    train-logloss:0.441688 
## [791]    train-logloss:0.441636 
## [792]    train-logloss:0.441584 
## [793]    train-logloss:0.441440 
## [794]    train-logloss:0.441340 
## [795]    train-logloss:0.441198 
## [796]    train-logloss:0.441146 
## [797]    train-logloss:0.441055 
## [798]    train-logloss:0.440957 
## [799]    train-logloss:0.440817 
## [800]    train-logloss:0.440767 
## [801]    train-logloss:0.440657 
## [802]    train-logloss:0.440565 
## [803]    train-logloss:0.440515 
## [804]    train-logloss:0.440377 
## [805]    train-logloss:0.440280 
## [806]    train-logloss:0.440180 
## [807]    train-logloss:0.440090 
## [808]    train-logloss:0.440041 
## [809]    train-logloss:0.439906 
## [810]    train-logloss:0.439798 
## [811]    train-logloss:0.439689 
## [812]    train-logloss:0.439637 
## [813]    train-logloss:0.439547 
## [814]    train-logloss:0.439448 
## [815]    train-logloss:0.439397 
## [816]    train-logloss:0.439308 
## [817]    train-logloss:0.439260 
## [818]    train-logloss:0.439169 
## [819]    train-logloss:0.439062 
## [820]    train-logloss:0.439010 
## [821]    train-logloss:0.438962 
## [822]    train-logloss:0.438864 
## [823]    train-logloss:0.438777 
## [824]    train-logloss:0.438643 
## [825]    train-logloss:0.438548 
## [826]    train-logloss:0.438443 
## [827]    train-logloss:0.438396 
## [828]    train-logloss:0.438298 
## [829]    train-logloss:0.438211 
## [830]    train-logloss:0.438164 
## [831]    train-logloss:0.438033 
## [832]    train-logloss:0.437940 
## [833]    train-logloss:0.437811 
## [834]    train-logloss:0.437715 
## [835]    train-logloss:0.437629 
## [836]    train-logloss:0.437582 
## [837]    train-logloss:0.437531 
## [838]    train-logloss:0.437445 
## [839]    train-logloss:0.437341 
## [840]    train-logloss:0.437290 
## [841]    train-logloss:0.437184 
## [842]    train-logloss:0.437133 
## [843]    train-logloss:0.437087 
## [844]    train-logloss:0.436985 
## [845]    train-logloss:0.436895 
## [846]    train-logloss:0.436850 
## [847]    train-logloss:0.436764 
## [848]    train-logloss:0.436714 
## [849]    train-logloss:0.436669 
## [850]    train-logloss:0.436574 
## [851]    train-logloss:0.436446 
## [852]    train-logloss:0.436355 
## [853]    train-logloss:0.436271 
## [854]    train-logloss:0.436181 
## [855]    train-logloss:0.436055 
## [856]    train-logloss:0.435955 
## [857]    train-logloss:0.435861 
## [858]    train-logloss:0.435817 
## [859]    train-logloss:0.435733 
## [860]    train-logloss:0.435644 
## [861]    train-logloss:0.435546 
## [862]    train-logloss:0.435502 
## [863]    train-logloss:0.435452 
## [864]    train-logloss:0.435346 
## [865]    train-logloss:0.435297 
## [866]    train-logloss:0.435253 
## [867]    train-logloss:0.435170 
## [868]    train-logloss:0.435046 
## [869]    train-logloss:0.434957 
## [870]    train-logloss:0.434861 
## [871]    train-logloss:0.434773 
## [872]    train-logloss:0.434730 
## [873]    train-logloss:0.434608 
## [874]    train-logloss:0.434515 
## [875]    train-logloss:0.434420 
## [876]    train-logloss:0.434377 
## [877]    train-logloss:0.434327 
## [878]    train-logloss:0.434224 
## [879]    train-logloss:0.434141 
## [880]    train-logloss:0.434092 
## [881]    train-logloss:0.434050 
## [882]    train-logloss:0.433956 
## [883]    train-logloss:0.433869 
## [884]    train-logloss:0.433821 
## [885]    train-logloss:0.433778 
## [886]    train-logloss:0.433658 
## [887]    train-logloss:0.433576 
## [888]    train-logloss:0.433484 
## [889]    train-logloss:0.433391 
## [890]    train-logloss:0.433289 
## [891]    train-logloss:0.433241 
## [892]    train-logloss:0.433199 
## [893]    train-logloss:0.433112 
## [894]    train-logloss:0.432994 
## [895]    train-logloss:0.432912 
## [896]    train-logloss:0.432827 
## [897]    train-logloss:0.432710 
## [898]    train-logloss:0.432619 
## [899]    train-logloss:0.432578 
## [900]    train-logloss:0.432486 
## [901]    train-logloss:0.432454 
## [902]    train-logloss:0.432374 
## [903]    train-logloss:0.432289 
## [904]    train-logloss:0.432174 
## [905]    train-logloss:0.432088 
## [906]    train-logloss:0.431987 
## [907]    train-logloss:0.431897 
## [908]    train-logloss:0.431848 
## [909]    train-logloss:0.431800 
## [910]    train-logloss:0.431768 
## [911]    train-logloss:0.431678 
## [912]    train-logloss:0.431598 
## [913]    train-logloss:0.431485 
## [914]    train-logloss:0.431401 
## [915]    train-logloss:0.431289 
## [916]    train-logloss:0.431199 
## [917]    train-logloss:0.431120 
## [918]    train-logloss:0.431020 
## [919]    train-logloss:0.430931 
## [920]    train-logloss:0.430847 
## [921]    train-logloss:0.430816 
## [922]    train-logloss:0.430737 
## [923]    train-logloss:0.430689 
## [924]    train-logloss:0.430658 
## [925]    train-logloss:0.430569 
## [926]    train-logloss:0.430491 
## [927]    train-logloss:0.430403 
## [928]    train-logloss:0.430305 
## [929]    train-logloss:0.430257 
## [930]    train-logloss:0.430174 
## [931]    train-logloss:0.430064 
## [932]    train-logloss:0.429982 
## [933]    train-logloss:0.429873 
## [934]    train-logloss:0.429785 
## [935]    train-logloss:0.429707 
## [936]    train-logloss:0.429620 
## [937]    train-logloss:0.429590 
## [938]    train-logloss:0.429503 
## [939]    train-logloss:0.429426 
## [940]    train-logloss:0.429378 
## [941]    train-logloss:0.429348 
## [942]    train-logloss:0.429241 
## [943]    train-logloss:0.429154 
## [944]    train-logloss:0.429074 
## [945]    train-logloss:0.428968 
## [946]    train-logloss:0.428891 
## [947]    train-logloss:0.428793 
## [948]    train-logloss:0.428708 
## [949]    train-logloss:0.428625 
## [950]    train-logloss:0.428595 
## [951]    train-logloss:0.428548 
## [952]    train-logloss:0.428501 
## [953]    train-logloss:0.428425 
## [954]    train-logloss:0.428345 
## [955]    train-logloss:0.428240 
## [956]    train-logloss:0.428144 
## [957]    train-logloss:0.428060 
## [958]    train-logloss:0.428031 
## [959]    train-logloss:0.427945 
## [960]    train-logloss:0.427899 
## [961]    train-logloss:0.427804 
## [962]    train-logloss:0.427721 
## [963]    train-logloss:0.427638 
## [964]    train-logloss:0.427592 
## [965]    train-logloss:0.427563 
## [966]    train-logloss:0.427459 
## [967]    train-logloss:0.427384 
## [968]    train-logloss:0.427305 
## [969]    train-logloss:0.427203 
## [970]    train-logloss:0.427118 
## [971]    train-logloss:0.427040 
## [972]    train-logloss:0.427012 
## [973]    train-logloss:0.426911 
## [974]    train-logloss:0.426836 
## [975]    train-logloss:0.426751 
## [976]    train-logloss:0.426676 
## [977]    train-logloss:0.426599 
## [978]    train-logloss:0.426500 
## [979]    train-logloss:0.426405 
## [980]    train-logloss:0.426321 
## [981]    train-logloss:0.426247 
## [982]    train-logloss:0.426150 
## [983]    train-logloss:0.426122 
## [984]    train-logloss:0.426076 
## [985]    train-logloss:0.426002 
## [986]    train-logloss:0.425956 
## [987]    train-logloss:0.425875 
## [988]    train-logloss:0.425782 
## [989]    train-logloss:0.425736 
## [990]    train-logloss:0.425655 
## [991]    train-logloss:0.425627 
## [992]    train-logloss:0.425547 
## [993]    train-logloss:0.425470 
## [994]    train-logloss:0.425373 
## [995]    train-logloss:0.425281 
## [996]    train-logloss:0.425205 
## [997]    train-logloss:0.425110 
## [998]    train-logloss:0.425026 
## [999]    train-logloss:0.424999 
## [1000]   train-logloss:0.424926

## Make predictions on test data 
moklas1.test.matrix<-data.matrix(moklas1.test[,-10])
promo.test<-as.matrix(as.factor(as.character(moklas1.test$promo)))
predicted <- predict(xgbModel3,moklas1.test.matrix )

predicted <- ifelse(predicted > 0.5 , 1,0)

## Create confusion matrix

confusionMatrix(table(predicted = predicted, actual = promo.test))

## Confusion Matrix and Statistics
## 
##          actual
## predicted  0  1
##         0 33  4
##         1 14 20
##                                           
##                Accuracy : 0.7465          
##                  95% CI : (0.6292, 0.8423)
##     No Information Rate : 0.662           
##     P-Value [Acc > NIR] : 0.08154         
##                                           
##                   Kappa : 0.4859          
##                                           
##  Mcnemar's Test P-Value : 0.03389         
##                                           
##             Sensitivity : 0.7021          
##             Specificity : 0.8333          
##          Pos Pred Value : 0.8919          
##          Neg Pred Value : 0.5882          
##              Prevalence : 0.6620          
##          Detection Rate : 0.4648          
##    Detection Prevalence : 0.5211          
##       Balanced Accuracy : 0.7677          
##                                           
##        'Positive' Class : 0               
##