Smatic-Analisis

DATA UNDERSTANDING

LOAD DATA AND LIBRARY

IMPORT PACKAGE

library(tidyverse)

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library(ggplot2)
library(corrplot) 

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library(corrgram)

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library(ROCR)

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library(psych)

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library(reshape2)

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library(VIM)

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library(randomForest)

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library(mlbench)

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library(caret)

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library(e1071)
library(AppliedPredictiveModeling)

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library(imbalance)
library(plyr)

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library(dplyr)
library(naivebayes)

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library(rpart)
library(LogicReg)

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library(rpart.plot)

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library(openxlsx)
library(skimr)
library(ada)
library(validate)

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library(DataExplorer)

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library(themis)

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library(hrbrthemes)

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library(dplyr)
library(tidyr)
library(viridis)

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IMPORT DATA

df<-read.csv("D:/Smatic/Soal Babak Semifinal Olimpiade SMATIC 4.0/Data/student-mat.csv",sep = ";")
df<-df%>%
  mutate_at(c("school","sex","address","famsize","Pstatus",
         "Medu","Fedu","Mjob","Fjob","reason","guardian","traveltime",
         "studytime","failures","schoolsup","famsup","paid","activities","nursery",
         "higher","internet","romantic","famrel",
         "freetime","goout","Dalc","Walc","health"),as.factor)

VARIABEL BARU

df<-df%>%
  mutate(y=ifelse(G3>9,"PASS","FAIL"))%>%
    mutate_at("y",as.factor)

STATISTIK DESKRIPTIF VARIABEL KONTINU DALAM DATASET

variabel<-c("Age","Absences","G1","G2","G3")
Minimum<-df%>%
  dplyr::select(age,absences,G1,G2,G3)%>%
  apply(2,min)
Median<-df%>%
  dplyr::select(age,absences,G1,G2,G3)%>%
  apply(2,median)
Maximum<-df%>%
  dplyr::select(age,absences,G1,G2,G3)%>%
  apply(2,max)
SD<-df%>%
  dplyr::select(age,absences,G1,G2,G3)%>%
  apply(2,sd)
Rataan<-df%>%
  dplyr::select(age,absences,G1,G2,G3)%>%
  apply(2,mean)
data_summary<-data.frame(variabel,Minimum,Median,Maximum,SD,Rataan)
rownames(data_summary)<-NULL
data_summary[,2:6]<-round(data_summary[,2:6],2)
data_summary

hist(df$age)

hist(df$absences)

hist(df$G1)

hist(df$G2)

hist(df$G3)

skew(df$age)

## [1] 0.4627348

HUBUNGAN VARIABEL PREDIKTOR KATEGORIK TERHADAP G (INFORMASI UMUM SISWA)

df%>%
  dplyr::group_by(df$school)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$sex)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$address)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

HUBUNGAN VARIABEL PREDIKTOR KATEGORIK TERHADAP G (KEGIATAN SISWA DI LUAR JAM SEKOLAH)

df%>%
  dplyr::group_by(df$paid)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$schoolsup)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$nursery)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$famsup)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$activities)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$romantic)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

HUBUNGAN VARIABEL PREDIKTOR KATEGORIK TERHADAP G (LINGKUNGAN RUMAH)

df%>%
  dplyr::group_by(df$famsize)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$Pstatus)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

df%>%
  dplyr::group_by(df$internet)%>%
  dplyr::summarise(g1=mean(G1),g2=mean(G2),g3=mean(G3),n=n())

SOSIODEMOGRAFI ORANGTUA

par(mfrow=c(2,2))
df%>%
  dplyr::group_by(Mjob)%>%
  dplyr::summarize(g1=mean(G1),g2=mean(G2),g3=mean(G3))%>%
  ggplot(aes(x=Mjob))+geom_line(aes(y=g1),color="#B31312",group=1,lwd=2)+geom_line(aes(y=g2),color="#19A7CE",group=1,lwd=2)+geom_line(aes(y=g3),color="#8B1874",group=1,lwd=2)+
  labs(title = "Pengaruh Pekerjaan Ibu terhadap Prestasi Siswa",y="Nilai Ujian Matematika",subtitle = "merah = G1, biru = G2, ungu = G3")

df%>%
  dplyr::group_by(Medu)%>%
  dplyr::summarize(g1=mean(G1),g2=mean(G2),g3=mean(G3))%>%
  ggplot(aes(x=Medu))+geom_line(aes(y=g1),color="#B31312",group=1,lwd=2)+geom_line(aes(y=g2),color="#19A7CE",group=1,lwd=2)+geom_line(aes(y=g3),color="#8B1874",group=1,lwd=2)+
  labs(title = "Pengaruh Pendidikan Ibu terhadap Prestasi Siswa",y="Nilai Ujian Matematika",subtitle = "merah = G1, biru = G2, ungu = G3")

df%>%
  dplyr::group_by(Fjob)%>%
  dplyr::summarize(g1=mean(G1),g2=mean(G2),g3=mean(G3))%>%
  ggplot(aes(x=Fjob))+geom_line(aes(y=g1),color="#B31312",group=1,lwd=2)+geom_line(aes(y=g2),color="#19A7CE",group=1,lwd=2)+geom_line(aes(y=g3),color="#8B1874",group=1,lwd=2)+
  labs(title = "Pengaruh Pekerjaan Ayah terhadap Prestasi Siswa",y="Nilai Ujian Matematika",subtitle = "merah = G1, biru = G2, ungu = G3")

df%>%
  dplyr::group_by(Fedu)%>%
  dplyr::summarize(g1=mean(G1),g2=mean(G2),g3=mean(G3))%>%
  ggplot(aes(x=Fedu))+geom_line(aes(y=g1),color="#B31312",group=1,lwd=2)+geom_line(aes(y=g2),color="#19A7CE",group=1,lwd=2)+geom_line(aes(y=g3),color="#8B1874",group=1,lwd=2)+
  labs(title = "Pengaruh Pendidikan Ayah terhadap Prestasi Siswa",y="Nilai Ujian Matematika",subtitle = "merah = G1, biru = G2, ungu = G3")

Analisis Korelasi

Periksa korelasi antar fitur.

df

cordata <- data.matrix(df[,c(1,2,3,4,5,6,7,33)])
cormat <- round(cor(cordata, method = "pearson"),2)
melted_cormat <- melt(cormat)


# Peroleh segitiga bawah dari matriks korelasi
  get_lower_tri<-function(cormat){
    cormat[upper.tri(cormat)] <- NA
    return(cormat)
  }
# Peroleh segitiga atas dari matriks korelasi
  get_upper_tri <- function(cormat){
    cormat[lower.tri(cormat)]<- NA
    return(cormat)
  }

upper_tri <- get_upper_tri(cormat)
# Atur matriks korelasi
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Buat ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
 geom_tile(color = "white")+
 scale_fill_gradient2(low = "blue", high = "red", mid = "white",
   midpoint = 0, limit = c(-1,1), space = "Lab",
    name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
 theme(axis.text.x = element_text(angle = 45, vjust = 1,
    size = 9, hjust = 1))+
 coord_fixed()

ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 2) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.6, 0.7),
  legend.direction = "horizontal")+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

cordata <- data.matrix(df[,c(8,9,10,11,12,13,14,33)])
cormat <- round(cor(cordata, method = "pearson"),2)
melted_cormat <- melt(cormat)


# Peroleh segitiga bawah dari matriks korelasi
  get_lower_tri<-function(cormat){
    cormat[upper.tri(cormat)] <- NA
    return(cormat)
  }
# Peroleh segitiga atas dari matriks korelasi
  get_upper_tri <- function(cormat){
    cormat[lower.tri(cormat)]<- NA
    return(cormat)
  }

upper_tri <- get_upper_tri(cormat)
# Atur matriks korelasi
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Buat ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
 geom_tile(color = "white")+
 scale_fill_gradient2(low = "blue", high = "red", mid = "white",
   midpoint = 0, limit = c(-1,1), space = "Lab",
    name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
 theme(axis.text.x = element_text(angle = 45, vjust = 1,
    size = 9, hjust = 1))+
 coord_fixed()

ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 2) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.6, 0.7),
  legend.direction = "horizontal")+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

cordata <- data.matrix(df[,c(15,16,17,18,19,20,21,22,23,33)])
cormat <- round(cor(cordata, method = "pearson"),2)
melted_cormat <- melt(cormat)


# Peroleh segitiga bawah dari matriks korelasi
  get_lower_tri<-function(cormat){
    cormat[upper.tri(cormat)] <- NA
    return(cormat)
  }
# Peroleh segitiga atas dari matriks korelasi
  get_upper_tri <- function(cormat){
    cormat[lower.tri(cormat)]<- NA
    return(cormat)
  }

upper_tri <- get_upper_tri(cormat)
# Atur matriks korelasi
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Buat ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
 geom_tile(color = "white")+
 scale_fill_gradient2(low = "blue", high = "red", mid = "white",
   midpoint = 0, limit = c(-1,1), space = "Lab",
    name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
 theme(axis.text.x = element_text(angle = 45, vjust = 1,
    size = 9, hjust = 1))+
 coord_fixed()

ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 2) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.6, 0.7),
  legend.direction = "horizontal")+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

cordata <- data.matrix(df[,c(24,25,26,27,28,29,30,31,32,33)])
cormat <- round(cor(cordata, method = "pearson"),2)
melted_cormat <- melt(cormat)


# Peroleh segitiga bawah dari matriks korelasi
  get_lower_tri<-function(cormat){
    cormat[upper.tri(cormat)] <- NA
    return(cormat)
  }
# Peroleh segitiga atas dari matriks korelasi
  get_upper_tri <- function(cormat){
    cormat[lower.tri(cormat)]<- NA
    return(cormat)
  }

upper_tri <- get_upper_tri(cormat)
# Atur matriks korelasi
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Buat ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
 geom_tile(color = "white")+
 scale_fill_gradient2(low = "blue", high = "red", mid = "white",
   midpoint = 0, limit = c(-1,1), space = "Lab",
    name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
 theme(axis.text.x = element_text(angle = 45, vjust = 1,
    size = 9, hjust = 1))+
 coord_fixed()

ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 2) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.6, 0.7),
  legend.direction = "horizontal")+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

DISTRIBUSI NILAI G3 BERDASARKAN KLASIFIKASI BINER ERASMUS

table(df$y)

## 
## FAIL PASS 
##  130  265

prop.table(table(df$y))

## 
##      FAIL      PASS 
## 0.3291139 0.6708861

p<-ggplot(df,aes(x=y,fill=y))+
  geom_bar()+labs(title = "Jumlah Siswa Berdasarkan Klasifikasi Erasmus",y="Jumlah Siswa",x="Keterangan")+
  scale_fill_manual(values=c("#B70404","#1D267D"))
p

PARTISI DATASET

set.seed(1234)
id_train <- createDataPartition(df$y,
                                p = 0.9,
                                list = F)
data_train <- df[id_train,]
data_valid <- df[-id_train,]

TUNING PARAMATER DAN PEMBENTUKAN BEBERAPA MODEL

1. XGBOOST

set.seed(123)
train_control <- trainControl(
  method = "repeatedcv",
  number = 10,
  repeats = 2,
  search = "random",
  verboseIter = T
)
set.seed(1234)
modelxgboost <-caret::train(y~school+sex+age+address+famsize+Pstatus+Medu+Fedu+Mjob+Fjob+reason+
                              guardian+traveltime+studytime+failures+schoolsup+famsup+paid+
                            activities+nursery+higher+internet+romantic+famrel+freetime+
                              goout+Dalc+Walc+health+absences+G1+G2,
                       trControl = train_control,
                       tuneLength = 10,
                       data = data_train,
                       method = "xgbTree")

## + Fold01.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold01.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold01.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold01.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold01.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold01.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold01.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold01.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold01.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold01.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold01.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold01.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold01.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold01.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold01.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold01.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold01.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold01.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold01.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold01.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold02.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold02.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold02.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold02.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold02.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold02.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold02.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold02.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold02.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold02.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold02.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold02.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold02.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold02.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold02.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold02.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold02.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold02.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold02.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold02.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold03.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold03.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold03.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold03.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold03.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold03.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold03.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold03.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold03.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold03.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold03.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold03.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold03.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold03.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold03.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold03.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold03.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold03.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold03.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold03.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold04.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold04.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold04.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold04.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold04.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold04.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold04.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold04.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold04.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold04.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold04.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold04.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold04.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold04.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold04.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold04.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold04.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold04.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold04.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold04.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold05.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold05.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold05.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold05.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold05.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold05.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold05.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold05.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold05.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold05.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold05.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold05.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold05.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold05.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold05.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold05.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold05.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold05.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold05.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold05.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold06.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold06.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold06.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold06.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold06.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold06.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold06.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold06.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold06.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold06.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold06.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold06.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold06.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold06.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold06.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold06.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold06.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold06.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold06.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold06.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold07.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold07.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold07.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold07.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold07.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold07.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold07.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold07.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold07.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold07.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold07.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold07.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold07.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold07.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold07.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold07.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold07.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold07.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold07.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold07.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold08.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold08.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold08.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold08.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold08.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold08.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold08.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold08.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold08.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold08.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold08.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold08.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold08.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold08.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold08.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold08.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold08.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold08.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold08.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold08.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold09.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold09.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold09.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold09.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold09.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold09.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold09.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold09.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold09.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold09.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold09.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold09.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold09.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold09.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold09.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold09.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold09.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold09.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold09.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold09.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold10.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold10.Rep1: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold10.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold10.Rep1: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold10.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold10.Rep1: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold10.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold10.Rep1: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold10.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold10.Rep1: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold10.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold10.Rep1: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold10.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold10.Rep1: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold10.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold10.Rep1: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold10.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold10.Rep1: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold10.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold10.Rep1: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold01.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold01.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold01.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold01.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold01.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold01.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold01.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold01.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold01.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold01.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold01.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold01.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold01.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold01.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold01.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold01.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold01.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold01.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold01.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold01.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold02.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold02.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold02.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold02.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold02.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold02.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold02.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold02.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold02.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold02.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold02.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold02.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold02.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold02.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold02.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold02.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold02.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold02.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold02.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold02.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold03.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold03.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold03.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold03.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold03.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold03.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold03.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold03.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold03.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold03.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold03.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold03.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold03.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold03.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold03.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold03.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold03.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold03.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold03.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold03.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold04.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold04.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold04.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold04.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold04.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold04.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold04.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold04.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold04.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold04.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold04.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold04.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold04.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold04.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold04.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold04.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold04.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold04.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold04.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold04.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold05.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold05.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold05.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold05.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold05.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold05.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold05.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold05.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold05.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold05.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold05.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold05.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold05.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold05.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold05.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold05.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold05.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold05.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold05.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold05.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold06.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold06.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold06.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold06.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold06.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold06.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold06.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold06.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold06.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold06.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold06.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold06.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold06.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold06.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold06.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold06.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold06.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold06.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold06.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold06.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold07.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold07.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold07.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold07.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold07.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold07.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold07.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold07.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold07.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold07.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold07.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold07.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold07.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold07.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold07.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold07.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold07.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold07.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold07.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold07.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold08.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold08.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold08.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold08.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold08.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold08.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold08.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold08.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold08.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold08.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold08.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold08.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold08.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold08.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold08.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold08.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold08.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold08.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold08.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold08.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold09.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold09.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold09.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold09.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold09.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold09.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold09.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold09.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold09.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold09.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold09.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold09.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold09.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold09.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold09.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold09.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold09.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold09.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold09.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold09.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## + Fold10.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## - Fold10.Rep2: eta=0.02842, max_depth= 6, gamma=8.074, colsample_bytree=0.6062, min_child_weight=16, subsample=0.5382, nrounds=918 
## + Fold10.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## - Fold10.Rep2: eta=0.10948, max_depth= 4, gamma=3.298, colsample_bytree=0.3612, min_child_weight=16, subsample=0.6039, nrounds=934 
## + Fold10.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## - Fold10.Rep2: eta=0.12155, max_depth= 5, gamma=6.771, colsample_bytree=0.4976, min_child_weight=16, subsample=0.6582, nrounds=900 
## + Fold10.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## - Fold10.Rep2: eta=0.15603, max_depth= 8, gamma=4.850, colsample_bytree=0.6005, min_child_weight= 7, subsample=0.3971, nrounds= 98 
## + Fold10.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## - Fold10.Rep2: eta=0.15985, max_depth= 6, gamma=6.464, colsample_bytree=0.4239, min_child_weight=18, subsample=0.4905, nrounds=623 
## + Fold10.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## - Fold10.Rep2: eta=0.18350, max_depth= 4, gamma=3.118, colsample_bytree=0.5869, min_child_weight= 5, subsample=0.7514, nrounds=905 
## + Fold10.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## - Fold10.Rep2: eta=0.27420, max_depth=10, gamma=5.533, colsample_bytree=0.3295, min_child_weight= 5, subsample=0.3025, nrounds=101 
## + Fold10.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## - Fold10.Rep2: eta=0.30488, max_depth= 8, gamma=6.218, colsample_bytree=0.5018, min_child_weight=16, subsample=0.9448, nrounds=645 
## + Fold10.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## - Fold10.Rep2: eta=0.45604, max_depth= 4, gamma=5.020, colsample_bytree=0.5016, min_child_weight= 7, subsample=0.3570, nrounds=400 
## + Fold10.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## - Fold10.Rep2: eta=0.59530, max_depth= 4, gamma=2.439, colsample_bytree=0.3699, min_child_weight= 9, subsample=0.9239, nrounds=103 
## Aggregating results
## Selecting tuning parameters
## Fitting nrounds = 645, max_depth = 8, eta = 0.305, gamma = 6.22, colsample_bytree = 0.502, min_child_weight = 16, subsample = 0.945 on full training set

modelxgboost

## eXtreme Gradient Boosting 
## 
## 356 samples
##  32 predictor
##   2 classes: 'FAIL', 'PASS' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 2 times) 
## Summary of sample sizes: 320, 321, 320, 320, 320, 322, ... 
## Resampling results across tuning parameters:
## 
##   eta         max_depth  gamma     colsample_bytree  min_child_weight
##   0.02841639   6         8.073523  0.6061839         16              
##   0.10947663   4         3.297702  0.3611996         16              
##   0.12154757   5         6.770945  0.4975844         16              
##   0.15602708   8         4.849912  0.6004801          7              
##   0.15984682   6         6.464061  0.4238746         18              
##   0.18349865   4         3.118243  0.5869087          5              
##   0.27419880  10         5.533336  0.3295120          5              
##   0.30487682   8         6.218192  0.5018184         16              
##   0.45604271   4         5.019975  0.5015734          7              
##   0.59529810   4         2.439288  0.3698599          9              
##   subsample  nrounds  Accuracy   Kappa    
##   0.5382000  918      0.9145938  0.8100444
##   0.6039323  934      0.9160621  0.8146312
##   0.6582023  900      0.9174113  0.8172683
##   0.3971310   98      0.9188002  0.8227077
##   0.4904833  623      0.7392367  0.2322857
##   0.7513715  905      0.9201914  0.8235712
##   0.3025394  101      0.9159827  0.8157416
##   0.9448004  645      0.9216176  0.8286691
##   0.3569615  400      0.9202288  0.8253158
##   0.9239354  103      0.9145542  0.8121408
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were nrounds = 645, max_depth = 8, eta
##  = 0.3048768, gamma = 6.218192, colsample_bytree = 0.5018184,
##  min_child_weight = 16 and subsample = 0.9448004.

confusion_matrix<-caret::confusionMatrix(data = predict(modelxgboost,
                               data_valid),
                data_valid$y,
                positive = "FAIL")
Accuracy<-confusion_matrix[["overall"]]["Accuracy"] #Penghitungan Accuracy
Precision<-confusion_matrix[["byClass"]]["Pos Pred Value"] #Penghitungan Presisi
Recall<-confusion_matrix[["byClass"]]["Sensitivity"] #Penghitungan Recall/Sensitivity
F1_Score1<-(2*Precision*Recall)/(Precision+Recall) #Penghitungan F1 Score
data_metric_xg<-data.frame(Metric=c("Akurasi","Presisi","Recall","F1_Score"),Nilai=c(Accuracy,Precision,Recall,F1_Score1))
data_metric_xg

modelxgboost$bestTune

2. ADABOOST

set.seed(1234)
train_control <- trainControl(
  method = "repeatedcv",
  number = 10,
  repeats = 2,
  search = "random",
  verboseIter = T
)

set.seed(1234)
model_adaboost <- caret::train(y~school+sex+age+address+famsize+Pstatus+Medu+Fedu+Mjob+Fjob+reason+
                              guardian+traveltime+studytime+failures+schoolsup+famsup+paid+
                            activities+nursery+higher+internet+romantic+famrel+freetime+
                              goout+Dalc+Walc+health+absences+G1+G2,
                       trControl = train_control,
                       tuneLength = 10,
                       data = data_train,
                       method = "AdaBoost.M1")

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## + Fold06.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## - Fold06.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## + Fold06.Rep2: coeflearn=Zhu, maxdepth=14, mfinal= 9 
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## + Fold06.Rep2: coeflearn=Zhu, maxdepth=15, mfinal=22 
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## + Fold06.Rep2: coeflearn=Zhu, maxdepth=20, mfinal= 5 
## - Fold06.Rep2: coeflearn=Zhu, maxdepth=20, mfinal= 5 
## + Fold06.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## - Fold06.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## + Fold07.Rep2: coeflearn=Breiman, maxdepth=14, mfinal=38 
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## + Fold07.Rep2: coeflearn=Breiman, maxdepth=24, mfinal= 4 
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## + Fold07.Rep2: coeflearn=Freund, maxdepth= 4, mfinal=86 
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## + Fold07.Rep2: coeflearn=Freund, maxdepth=30, mfinal=98 
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## + Fold07.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
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## + Fold08.Rep2: coeflearn=Breiman, maxdepth=14, mfinal=38 
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## + Fold08.Rep2: coeflearn=Breiman, maxdepth=24, mfinal= 4 
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## + Fold08.Rep2: coeflearn=Freund, maxdepth= 4, mfinal=86 
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## + Fold08.Rep2: coeflearn=Freund, maxdepth=30, mfinal=98 
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## + Fold08.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## - Fold08.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## + Fold08.Rep2: coeflearn=Zhu, maxdepth=14, mfinal= 9 
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## + Fold08.Rep2: coeflearn=Zhu, maxdepth=15, mfinal=22 
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## + Fold08.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## - Fold08.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## + Fold09.Rep2: coeflearn=Breiman, maxdepth=14, mfinal=38 
## - Fold09.Rep2: coeflearn=Breiman, maxdepth=14, mfinal=38 
## + Fold09.Rep2: coeflearn=Breiman, maxdepth=24, mfinal= 4 
## - Fold09.Rep2: coeflearn=Breiman, maxdepth=24, mfinal= 4 
## + Fold09.Rep2: coeflearn=Freund, maxdepth= 4, mfinal=86 
## - Fold09.Rep2: coeflearn=Freund, maxdepth= 4, mfinal=86 
## + Fold09.Rep2: coeflearn=Freund, maxdepth=30, mfinal=98 
## - Fold09.Rep2: coeflearn=Freund, maxdepth=30, mfinal=98 
## + Fold09.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## - Fold09.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## + Fold09.Rep2: coeflearn=Zhu, maxdepth=14, mfinal= 9 
## - Fold09.Rep2: coeflearn=Zhu, maxdepth=14, mfinal= 9 
## + Fold09.Rep2: coeflearn=Zhu, maxdepth=15, mfinal=22 
## - Fold09.Rep2: coeflearn=Zhu, maxdepth=15, mfinal=22 
## + Fold09.Rep2: coeflearn=Zhu, maxdepth=20, mfinal= 5 
## - Fold09.Rep2: coeflearn=Zhu, maxdepth=20, mfinal= 5 
## + Fold09.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## - Fold09.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## + Fold10.Rep2: coeflearn=Breiman, maxdepth=14, mfinal=38 
## - Fold10.Rep2: coeflearn=Breiman, maxdepth=14, mfinal=38 
## + Fold10.Rep2: coeflearn=Breiman, maxdepth=24, mfinal= 4 
## - Fold10.Rep2: coeflearn=Breiman, maxdepth=24, mfinal= 4 
## + Fold10.Rep2: coeflearn=Freund, maxdepth= 4, mfinal=86 
## - Fold10.Rep2: coeflearn=Freund, maxdepth= 4, mfinal=86 
## + Fold10.Rep2: coeflearn=Freund, maxdepth=30, mfinal=98 
## - Fold10.Rep2: coeflearn=Freund, maxdepth=30, mfinal=98 
## + Fold10.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## - Fold10.Rep2: coeflearn=Zhu, maxdepth= 4, mfinal=90 
## + Fold10.Rep2: coeflearn=Zhu, maxdepth=14, mfinal= 9 
## - Fold10.Rep2: coeflearn=Zhu, maxdepth=14, mfinal= 9 
## + Fold10.Rep2: coeflearn=Zhu, maxdepth=15, mfinal=22 
## - Fold10.Rep2: coeflearn=Zhu, maxdepth=15, mfinal=22 
## + Fold10.Rep2: coeflearn=Zhu, maxdepth=20, mfinal= 5 
## - Fold10.Rep2: coeflearn=Zhu, maxdepth=20, mfinal= 5 
## + Fold10.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## - Fold10.Rep2: coeflearn=Zhu, maxdepth=21, mfinal=70 
## Aggregating results
## Selecting tuning parameters
## Fitting mfinal = 22, maxdepth = 15, coeflearn = Zhu on full training set

model_adaboost$bestTune

confusion_matrix<-caret::confusionMatrix(data = predict(model_adaboost,
                               data_valid),
                data_valid$y,
                positive = "FAIL")
Accuracy<-confusion_matrix[["overall"]]["Accuracy"] #Penghitungan Accuracy
Precision<-confusion_matrix[["byClass"]]["Pos Pred Value"] #Penghitungan Presisi
Recall<-confusion_matrix[["byClass"]]["Sensitivity"] #Penghitungan Recall/Sensitivity
F1_Score2<-(2*Precision*Recall)/(Precision+Recall) #Penghitungan F1 Score
data_metric_ada<-data.frame(Metric=c("Akurasi","Presisi","Recall","F1_Score"),Nilai=c(Accuracy,Precision,Recall,F1_Score2))
data_metric_ada

3. RANDOM FOREST

set.seed(1234)
train_control <- trainControl(
  method = "repeatedcv",
  number = 10,
  repeats = 2,
  search = "random",
  verboseIter = T
)

set.seed(1234)
model_rf <- caret::train(y~school+sex+age+address+famsize+Pstatus+Medu+Fedu+Mjob+Fjob+reason+
                              guardian+traveltime+studytime+failures+schoolsup+famsup+paid+
                            activities+nursery+higher+internet+romantic+famrel+freetime+
                              goout+Dalc+Walc+health+absences+G1+G2,
                       trControl = train_control,
                       tuneLength = 10,
                       data = data_train,
                       method = "rf")

## + Fold01.Rep1: mtry=22 
## - Fold01.Rep1: mtry=22 
## + Fold01.Rep1: mtry= 9 
## - Fold01.Rep1: mtry= 9 
## + Fold01.Rep1: mtry= 5 
## - Fold01.Rep1: mtry= 5 
## + Fold01.Rep1: mtry=38 
## - Fold01.Rep1: mtry=38 
## + Fold01.Rep1: mtry=16 
## - Fold01.Rep1: mtry=16 
## + Fold01.Rep1: mtry= 4 
## - Fold01.Rep1: mtry= 4 
## + Fold01.Rep1: mtry=70 
## - Fold01.Rep1: mtry=70 
## + Fold01.Rep1: mtry=14 
## - Fold01.Rep1: mtry=14 
## + Fold01.Rep1: mtry=56 
## - Fold01.Rep1: mtry=56 
## + Fold01.Rep1: mtry=62 
## - Fold01.Rep1: mtry=62 
## + Fold02.Rep1: mtry=22 
## - Fold02.Rep1: mtry=22 
## + Fold02.Rep1: mtry= 9 
## - Fold02.Rep1: mtry= 9 
## + Fold02.Rep1: mtry= 5 
## - Fold02.Rep1: mtry= 5 
## + Fold02.Rep1: mtry=38 
## - Fold02.Rep1: mtry=38 
## + Fold02.Rep1: mtry=16 
## - Fold02.Rep1: mtry=16 
## + Fold02.Rep1: mtry= 4 
## - Fold02.Rep1: mtry= 4 
## + Fold02.Rep1: mtry=70 
## - Fold02.Rep1: mtry=70 
## + Fold02.Rep1: mtry=14 
## - Fold02.Rep1: mtry=14 
## + Fold02.Rep1: mtry=56 
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## + Fold02.Rep1: mtry=62 
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## + Fold03.Rep1: mtry=22 
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## + Fold03.Rep1: mtry= 9 
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## + Fold03.Rep1: mtry= 5 
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## + Fold03.Rep1: mtry=38 
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## + Fold03.Rep1: mtry=16 
## - Fold03.Rep1: mtry=16 
## + Fold03.Rep1: mtry= 4 
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## + Fold03.Rep1: mtry=70 
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## + Fold03.Rep1: mtry=14 
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## + Fold03.Rep1: mtry=56 
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## + Fold03.Rep1: mtry=62 
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## + Fold04.Rep1: mtry=22 
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## + Fold04.Rep1: mtry= 9 
## - Fold04.Rep1: mtry= 9 
## + Fold04.Rep1: mtry= 5 
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## + Fold04.Rep1: mtry=38 
## - Fold04.Rep1: mtry=38 
## + Fold04.Rep1: mtry=16 
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## + Fold04.Rep1: mtry= 4 
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## + Fold04.Rep1: mtry=70 
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## + Fold04.Rep1: mtry=14 
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## + Fold04.Rep1: mtry=56 
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## + Fold04.Rep1: mtry=62 
## - Fold04.Rep1: mtry=62 
## + Fold05.Rep1: mtry=22 
## - Fold05.Rep1: mtry=22 
## + Fold05.Rep1: mtry= 9 
## - Fold05.Rep1: mtry= 9 
## + Fold05.Rep1: mtry= 5 
## - Fold05.Rep1: mtry= 5 
## + Fold05.Rep1: mtry=38 
## - Fold05.Rep1: mtry=38 
## + Fold05.Rep1: mtry=16 
## - Fold05.Rep1: mtry=16 
## + Fold05.Rep1: mtry= 4 
## - Fold05.Rep1: mtry= 4 
## + Fold05.Rep1: mtry=70 
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## + Fold05.Rep1: mtry=14 
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## + Fold05.Rep1: mtry=56 
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## + Fold05.Rep1: mtry=62 
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## + Fold06.Rep1: mtry=22 
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## + Fold06.Rep1: mtry= 9 
## - Fold06.Rep1: mtry= 9 
## + Fold06.Rep1: mtry= 5 
## - Fold06.Rep1: mtry= 5 
## + Fold06.Rep1: mtry=38 
## - Fold06.Rep1: mtry=38 
## + Fold06.Rep1: mtry=16 
## - Fold06.Rep1: mtry=16 
## + Fold06.Rep1: mtry= 4 
## - Fold06.Rep1: mtry= 4 
## + Fold06.Rep1: mtry=70 
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## + Fold06.Rep1: mtry=14 
## - Fold06.Rep1: mtry=14 
## + Fold06.Rep1: mtry=56 
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## + Fold06.Rep1: mtry=62 
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## + Fold07.Rep1: mtry=22 
## - Fold07.Rep1: mtry=22 
## + Fold07.Rep1: mtry= 9 
## - Fold07.Rep1: mtry= 9 
## + Fold07.Rep1: mtry= 5 
## - Fold07.Rep1: mtry= 5 
## + Fold07.Rep1: mtry=38 
## - Fold07.Rep1: mtry=38 
## + Fold07.Rep1: mtry=16 
## - Fold07.Rep1: mtry=16 
## + Fold07.Rep1: mtry= 4 
## - Fold07.Rep1: mtry= 4 
## + Fold07.Rep1: mtry=70 
## - Fold07.Rep1: mtry=70 
## + Fold07.Rep1: mtry=14 
## - Fold07.Rep1: mtry=14 
## + Fold07.Rep1: mtry=56 
## - Fold07.Rep1: mtry=56 
## + Fold07.Rep1: mtry=62 
## - Fold07.Rep1: mtry=62 
## + Fold08.Rep1: mtry=22 
## - Fold08.Rep1: mtry=22 
## + Fold08.Rep1: mtry= 9 
## - Fold08.Rep1: mtry= 9 
## + Fold08.Rep1: mtry= 5 
## - Fold08.Rep1: mtry= 5 
## + Fold08.Rep1: mtry=38 
## - Fold08.Rep1: mtry=38 
## + Fold08.Rep1: mtry=16 
## - Fold08.Rep1: mtry=16 
## + Fold08.Rep1: mtry= 4 
## - Fold08.Rep1: mtry= 4 
## + Fold08.Rep1: mtry=70 
## - Fold08.Rep1: mtry=70 
## + Fold08.Rep1: mtry=14 
## - Fold08.Rep1: mtry=14 
## + Fold08.Rep1: mtry=56 
## - Fold08.Rep1: mtry=56 
## + Fold08.Rep1: mtry=62 
## - Fold08.Rep1: mtry=62 
## + Fold09.Rep1: mtry=22 
## - Fold09.Rep1: mtry=22 
## + Fold09.Rep1: mtry= 9 
## - Fold09.Rep1: mtry= 9 
## + Fold09.Rep1: mtry= 5 
## - Fold09.Rep1: mtry= 5 
## + Fold09.Rep1: mtry=38 
## - Fold09.Rep1: mtry=38 
## + Fold09.Rep1: mtry=16 
## - Fold09.Rep1: mtry=16 
## + Fold09.Rep1: mtry= 4 
## - Fold09.Rep1: mtry= 4 
## + Fold09.Rep1: mtry=70 
## - Fold09.Rep1: mtry=70 
## + Fold09.Rep1: mtry=14 
## - Fold09.Rep1: mtry=14 
## + Fold09.Rep1: mtry=56 
## - Fold09.Rep1: mtry=56 
## + Fold09.Rep1: mtry=62 
## - Fold09.Rep1: mtry=62 
## + Fold10.Rep1: mtry=22 
## - Fold10.Rep1: mtry=22 
## + Fold10.Rep1: mtry= 9 
## - Fold10.Rep1: mtry= 9 
## + Fold10.Rep1: mtry= 5 
## - Fold10.Rep1: mtry= 5 
## + Fold10.Rep1: mtry=38 
## - Fold10.Rep1: mtry=38 
## + Fold10.Rep1: mtry=16 
## - Fold10.Rep1: mtry=16 
## + Fold10.Rep1: mtry= 4 
## - Fold10.Rep1: mtry= 4 
## + Fold10.Rep1: mtry=70 
## - Fold10.Rep1: mtry=70 
## + Fold10.Rep1: mtry=14 
## - Fold10.Rep1: mtry=14 
## + Fold10.Rep1: mtry=56 
## - Fold10.Rep1: mtry=56 
## + Fold10.Rep1: mtry=62 
## - Fold10.Rep1: mtry=62 
## + Fold01.Rep2: mtry=22 
## - Fold01.Rep2: mtry=22 
## + Fold01.Rep2: mtry= 9 
## - Fold01.Rep2: mtry= 9 
## + Fold01.Rep2: mtry= 5 
## - Fold01.Rep2: mtry= 5 
## + Fold01.Rep2: mtry=38 
## - Fold01.Rep2: mtry=38 
## + Fold01.Rep2: mtry=16 
## - Fold01.Rep2: mtry=16 
## + Fold01.Rep2: mtry= 4 
## - Fold01.Rep2: mtry= 4 
## + Fold01.Rep2: mtry=70 
## - Fold01.Rep2: mtry=70 
## + Fold01.Rep2: mtry=14 
## - Fold01.Rep2: mtry=14 
## + Fold01.Rep2: mtry=56 
## - Fold01.Rep2: mtry=56 
## + Fold01.Rep2: mtry=62 
## - Fold01.Rep2: mtry=62 
## + Fold02.Rep2: mtry=22 
## - Fold02.Rep2: mtry=22 
## + Fold02.Rep2: mtry= 9 
## - Fold02.Rep2: mtry= 9 
## + Fold02.Rep2: mtry= 5 
## - Fold02.Rep2: mtry= 5 
## + Fold02.Rep2: mtry=38 
## - Fold02.Rep2: mtry=38 
## + Fold02.Rep2: mtry=16 
## - Fold02.Rep2: mtry=16 
## + Fold02.Rep2: mtry= 4 
## - Fold02.Rep2: mtry= 4 
## + Fold02.Rep2: mtry=70 
## - Fold02.Rep2: mtry=70 
## + Fold02.Rep2: mtry=14 
## - Fold02.Rep2: mtry=14 
## + Fold02.Rep2: mtry=56 
## - Fold02.Rep2: mtry=56 
## + Fold02.Rep2: mtry=62 
## - Fold02.Rep2: mtry=62 
## + Fold03.Rep2: mtry=22 
## - Fold03.Rep2: mtry=22 
## + Fold03.Rep2: mtry= 9 
## - Fold03.Rep2: mtry= 9 
## + Fold03.Rep2: mtry= 5 
## - Fold03.Rep2: mtry= 5 
## + Fold03.Rep2: mtry=38 
## - Fold03.Rep2: mtry=38 
## + Fold03.Rep2: mtry=16 
## - Fold03.Rep2: mtry=16 
## + Fold03.Rep2: mtry= 4 
## - Fold03.Rep2: mtry= 4 
## + Fold03.Rep2: mtry=70 
## - Fold03.Rep2: mtry=70 
## + Fold03.Rep2: mtry=14 
## - Fold03.Rep2: mtry=14 
## + Fold03.Rep2: mtry=56 
## - Fold03.Rep2: mtry=56 
## + Fold03.Rep2: mtry=62 
## - Fold03.Rep2: mtry=62 
## + Fold04.Rep2: mtry=22 
## - Fold04.Rep2: mtry=22 
## + Fold04.Rep2: mtry= 9 
## - Fold04.Rep2: mtry= 9 
## + Fold04.Rep2: mtry= 5 
## - Fold04.Rep2: mtry= 5 
## + Fold04.Rep2: mtry=38 
## - Fold04.Rep2: mtry=38 
## + Fold04.Rep2: mtry=16 
## - Fold04.Rep2: mtry=16 
## + Fold04.Rep2: mtry= 4 
## - Fold04.Rep2: mtry= 4 
## + Fold04.Rep2: mtry=70 
## - Fold04.Rep2: mtry=70 
## + Fold04.Rep2: mtry=14 
## - Fold04.Rep2: mtry=14 
## + Fold04.Rep2: mtry=56 
## - Fold04.Rep2: mtry=56 
## + Fold04.Rep2: mtry=62 
## - Fold04.Rep2: mtry=62 
## + Fold05.Rep2: mtry=22 
## - Fold05.Rep2: mtry=22 
## + Fold05.Rep2: mtry= 9 
## - Fold05.Rep2: mtry= 9 
## + Fold05.Rep2: mtry= 5 
## - Fold05.Rep2: mtry= 5 
## + Fold05.Rep2: mtry=38 
## - Fold05.Rep2: mtry=38 
## + Fold05.Rep2: mtry=16 
## - Fold05.Rep2: mtry=16 
## + Fold05.Rep2: mtry= 4 
## - Fold05.Rep2: mtry= 4 
## + Fold05.Rep2: mtry=70 
## - Fold05.Rep2: mtry=70 
## + Fold05.Rep2: mtry=14 
## - Fold05.Rep2: mtry=14 
## + Fold05.Rep2: mtry=56 
## - Fold05.Rep2: mtry=56 
## + Fold05.Rep2: mtry=62 
## - Fold05.Rep2: mtry=62 
## + Fold06.Rep2: mtry=22 
## - Fold06.Rep2: mtry=22 
## + Fold06.Rep2: mtry= 9 
## - Fold06.Rep2: mtry= 9 
## + Fold06.Rep2: mtry= 5 
## - Fold06.Rep2: mtry= 5 
## + Fold06.Rep2: mtry=38 
## - Fold06.Rep2: mtry=38 
## + Fold06.Rep2: mtry=16 
## - Fold06.Rep2: mtry=16 
## + Fold06.Rep2: mtry= 4 
## - Fold06.Rep2: mtry= 4 
## + Fold06.Rep2: mtry=70 
## - Fold06.Rep2: mtry=70 
## + Fold06.Rep2: mtry=14 
## - Fold06.Rep2: mtry=14 
## + Fold06.Rep2: mtry=56 
## - Fold06.Rep2: mtry=56 
## + Fold06.Rep2: mtry=62 
## - Fold06.Rep2: mtry=62 
## + Fold07.Rep2: mtry=22 
## - Fold07.Rep2: mtry=22 
## + Fold07.Rep2: mtry= 9 
## - Fold07.Rep2: mtry= 9 
## + Fold07.Rep2: mtry= 5 
## - Fold07.Rep2: mtry= 5 
## + Fold07.Rep2: mtry=38 
## - Fold07.Rep2: mtry=38 
## + Fold07.Rep2: mtry=16 
## - Fold07.Rep2: mtry=16 
## + Fold07.Rep2: mtry= 4 
## - Fold07.Rep2: mtry= 4 
## + Fold07.Rep2: mtry=70 
## - Fold07.Rep2: mtry=70 
## + Fold07.Rep2: mtry=14 
## - Fold07.Rep2: mtry=14 
## + Fold07.Rep2: mtry=56 
## - Fold07.Rep2: mtry=56 
## + Fold07.Rep2: mtry=62 
## - Fold07.Rep2: mtry=62 
## + Fold08.Rep2: mtry=22 
## - Fold08.Rep2: mtry=22 
## + Fold08.Rep2: mtry= 9 
## - Fold08.Rep2: mtry= 9 
## + Fold08.Rep2: mtry= 5 
## - Fold08.Rep2: mtry= 5 
## + Fold08.Rep2: mtry=38 
## - Fold08.Rep2: mtry=38 
## + Fold08.Rep2: mtry=16 
## - Fold08.Rep2: mtry=16 
## + Fold08.Rep2: mtry= 4 
## - Fold08.Rep2: mtry= 4 
## + Fold08.Rep2: mtry=70 
## - Fold08.Rep2: mtry=70 
## + Fold08.Rep2: mtry=14 
## - Fold08.Rep2: mtry=14 
## + Fold08.Rep2: mtry=56 
## - Fold08.Rep2: mtry=56 
## + Fold08.Rep2: mtry=62 
## - Fold08.Rep2: mtry=62 
## + Fold09.Rep2: mtry=22 
## - Fold09.Rep2: mtry=22 
## + Fold09.Rep2: mtry= 9 
## - Fold09.Rep2: mtry= 9 
## + Fold09.Rep2: mtry= 5 
## - Fold09.Rep2: mtry= 5 
## + Fold09.Rep2: mtry=38 
## - Fold09.Rep2: mtry=38 
## + Fold09.Rep2: mtry=16 
## - Fold09.Rep2: mtry=16 
## + Fold09.Rep2: mtry= 4 
## - Fold09.Rep2: mtry= 4 
## + Fold09.Rep2: mtry=70 
## - Fold09.Rep2: mtry=70 
## + Fold09.Rep2: mtry=14 
## - Fold09.Rep2: mtry=14 
## + Fold09.Rep2: mtry=56 
## - Fold09.Rep2: mtry=56 
## + Fold09.Rep2: mtry=62 
## - Fold09.Rep2: mtry=62 
## + Fold10.Rep2: mtry=22 
## - Fold10.Rep2: mtry=22 
## + Fold10.Rep2: mtry= 9 
## - Fold10.Rep2: mtry= 9 
## + Fold10.Rep2: mtry= 5 
## - Fold10.Rep2: mtry= 5 
## + Fold10.Rep2: mtry=38 
## - Fold10.Rep2: mtry=38 
## + Fold10.Rep2: mtry=16 
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## + Fold10.Rep2: mtry= 4 
## - Fold10.Rep2: mtry= 4 
## + Fold10.Rep2: mtry=70 
## - Fold10.Rep2: mtry=70 
## + Fold10.Rep2: mtry=14 
## - Fold10.Rep2: mtry=14 
## + Fold10.Rep2: mtry=56 
## - Fold10.Rep2: mtry=56 
## + Fold10.Rep2: mtry=62 
## - Fold10.Rep2: mtry=62 
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 38 on full training set

model_rf$bestTune

confusion_matrix<-caret::confusionMatrix(data = predict(model_rf,
                               data_valid),
                data_valid$y,
                positive = "FAIL")
Accuracy<-confusion_matrix[["overall"]]["Accuracy"] #Penghitungan Accuracy
Precision<-confusion_matrix[["byClass"]]["Pos Pred Value"] #Penghitungan Presisi
Recall<-confusion_matrix[["byClass"]]["Sensitivity"] #Penghitungan Recall/Sensitivity
F1_Score3<-(2*Precision*Recall)/(Precision+Recall) #Penghitungan F1 Score
data_metric_rf<-data.frame(Metric=c("Akurasi","Presisi","Recall","F1_Score"),Nilai=c(Accuracy,Precision,Recall,F1_Score3))
data_metric_rf

plot(varImp(model_rf),main="Variable Importance with Random Forest")

varImp(model_rf)

## rf variable importance
## 
##   only 20 most important variables shown (out of 71)
## 
##                Overall
## G2            100.0000
## G1             34.5588
## absences        3.9020
## age             3.8326
## Fjobother       2.9629
## famsizeLE3      1.5126
## famrel4         1.4498
## failures2       1.3884
## Fedu2           1.2191
## goout5          1.0805
## activitiesyes   1.0510
## schoolsupyes    1.0377
## Fedu1           1.0170
## romanticyes     0.9926
## famrel5         0.9452
## Dalc3           0.9133
## studytime3      0.9000
## health5         0.8489
## paidyes         0.8160
## freetime4       0.8114

variabel<-c("G2","G1","absences","age","Fjob","famsize","famrel","failures","fedu","goout","activities","schoolsup")
importance<-c(100,34.56,3.9,3.83,2.96,1.51,1.45,1.39,1.21,1.08,1.05,1.04)
dataimportance<-data.frame(variabel,importance)
ggplot(dataimportance)+
  geom_col(aes(x=importance,y=fct_reorder(variabel,importance),fill=importance))+labs(title = "Variable Important Plot",y="Variabel")+scale_color_manual(values = "blue")

#G2,G1,absences,age,Fjob,famsize,famrel,failures,Fedu,goout,activities,schoolsupyes

4. RANDOM FOREST BEST MODEL

set.seed(1234)
train_control <- trainControl(
  method = "repeatedcv",
  number = 10,
  repeats = 2,
  search = "random",
  verboseIter = T
)

set.seed(1234)
model_rf_best <- caret::train(y~G1+G2+absences+age+Fjob+famsize+famrel+failures+
                           Fedu+goout+activities+schoolsup,
                       trControl = train_control,
                       tuneLength = 10,
                       data = data_train,
                       method = "rf")

## + Fold01.Rep1: mtry=26 
## - Fold01.Rep1: mtry=26 
## + Fold01.Rep1: mtry=22 
## - Fold01.Rep1: mtry=22 
## + Fold01.Rep1: mtry= 5 
## - Fold01.Rep1: mtry= 5 
## + Fold01.Rep1: mtry=12 
## - Fold01.Rep1: mtry=12 
## + Fold01.Rep1: mtry=15 
## - Fold01.Rep1: mtry=15 
## + Fold01.Rep1: mtry= 9 
## - Fold01.Rep1: mtry= 9 
## + Fold01.Rep1: mtry= 6 
## - Fold01.Rep1: mtry= 6 
## + Fold01.Rep1: mtry=16 
## - Fold01.Rep1: mtry=16 
## + Fold01.Rep1: mtry= 4 
## - Fold01.Rep1: mtry= 4 
## + Fold02.Rep1: mtry=26 
## - Fold02.Rep1: mtry=26 
## + Fold02.Rep1: mtry=22 
## - Fold02.Rep1: mtry=22 
## + Fold02.Rep1: mtry= 5 
## - Fold02.Rep1: mtry= 5 
## + Fold02.Rep1: mtry=12 
## - Fold02.Rep1: mtry=12 
## + Fold02.Rep1: mtry=15 
## - Fold02.Rep1: mtry=15 
## + Fold02.Rep1: mtry= 9 
## - Fold02.Rep1: mtry= 9 
## + Fold02.Rep1: mtry= 6 
## - Fold02.Rep1: mtry= 6 
## + Fold02.Rep1: mtry=16 
## - Fold02.Rep1: mtry=16 
## + Fold02.Rep1: mtry= 4 
## - Fold02.Rep1: mtry= 4 
## + Fold03.Rep1: mtry=26 
## - Fold03.Rep1: mtry=26 
## + Fold03.Rep1: mtry=22 
## - Fold03.Rep1: mtry=22 
## + Fold03.Rep1: mtry= 5 
## - Fold03.Rep1: mtry= 5 
## + Fold03.Rep1: mtry=12 
## - Fold03.Rep1: mtry=12 
## + Fold03.Rep1: mtry=15 
## - Fold03.Rep1: mtry=15 
## + Fold03.Rep1: mtry= 9 
## - Fold03.Rep1: mtry= 9 
## + Fold03.Rep1: mtry= 6 
## - Fold03.Rep1: mtry= 6 
## + Fold03.Rep1: mtry=16 
## - Fold03.Rep1: mtry=16 
## + Fold03.Rep1: mtry= 4 
## - Fold03.Rep1: mtry= 4 
## + Fold04.Rep1: mtry=26 
## - Fold04.Rep1: mtry=26 
## + Fold04.Rep1: mtry=22 
## - Fold04.Rep1: mtry=22 
## + Fold04.Rep1: mtry= 5 
## - Fold04.Rep1: mtry= 5 
## + Fold04.Rep1: mtry=12 
## - Fold04.Rep1: mtry=12 
## + Fold04.Rep1: mtry=15 
## - Fold04.Rep1: mtry=15 
## + Fold04.Rep1: mtry= 9 
## - Fold04.Rep1: mtry= 9 
## + Fold04.Rep1: mtry= 6 
## - Fold04.Rep1: mtry= 6 
## + Fold04.Rep1: mtry=16 
## - Fold04.Rep1: mtry=16 
## + Fold04.Rep1: mtry= 4 
## - Fold04.Rep1: mtry= 4 
## + Fold05.Rep1: mtry=26 
## - Fold05.Rep1: mtry=26 
## + Fold05.Rep1: mtry=22 
## - Fold05.Rep1: mtry=22 
## + Fold05.Rep1: mtry= 5 
## - Fold05.Rep1: mtry= 5 
## + Fold05.Rep1: mtry=12 
## - Fold05.Rep1: mtry=12 
## + Fold05.Rep1: mtry=15 
## - Fold05.Rep1: mtry=15 
## + Fold05.Rep1: mtry= 9 
## - Fold05.Rep1: mtry= 9 
## + Fold05.Rep1: mtry= 6 
## - Fold05.Rep1: mtry= 6 
## + Fold05.Rep1: mtry=16 
## - Fold05.Rep1: mtry=16 
## + Fold05.Rep1: mtry= 4 
## - Fold05.Rep1: mtry= 4 
## + Fold06.Rep1: mtry=26 
## - Fold06.Rep1: mtry=26 
## + Fold06.Rep1: mtry=22 
## - Fold06.Rep1: mtry=22 
## + Fold06.Rep1: mtry= 5 
## - Fold06.Rep1: mtry= 5 
## + Fold06.Rep1: mtry=12 
## - Fold06.Rep1: mtry=12 
## + Fold06.Rep1: mtry=15 
## - Fold06.Rep1: mtry=15 
## + Fold06.Rep1: mtry= 9 
## - Fold06.Rep1: mtry= 9 
## + Fold06.Rep1: mtry= 6 
## - Fold06.Rep1: mtry= 6 
## + Fold06.Rep1: mtry=16 
## - Fold06.Rep1: mtry=16 
## + Fold06.Rep1: mtry= 4 
## - Fold06.Rep1: mtry= 4 
## + Fold07.Rep1: mtry=26 
## - Fold07.Rep1: mtry=26 
## + Fold07.Rep1: mtry=22 
## - Fold07.Rep1: mtry=22 
## + Fold07.Rep1: mtry= 5 
## - Fold07.Rep1: mtry= 5 
## + Fold07.Rep1: mtry=12 
## - Fold07.Rep1: mtry=12 
## + Fold07.Rep1: mtry=15 
## - Fold07.Rep1: mtry=15 
## + Fold07.Rep1: mtry= 9 
## - Fold07.Rep1: mtry= 9 
## + Fold07.Rep1: mtry= 6 
## - Fold07.Rep1: mtry= 6 
## + Fold07.Rep1: mtry=16 
## - Fold07.Rep1: mtry=16 
## + Fold07.Rep1: mtry= 4 
## - Fold07.Rep1: mtry= 4 
## + Fold08.Rep1: mtry=26 
## - Fold08.Rep1: mtry=26 
## + Fold08.Rep1: mtry=22 
## - Fold08.Rep1: mtry=22 
## + Fold08.Rep1: mtry= 5 
## - Fold08.Rep1: mtry= 5 
## + Fold08.Rep1: mtry=12 
## - Fold08.Rep1: mtry=12 
## + Fold08.Rep1: mtry=15 
## - Fold08.Rep1: mtry=15 
## + Fold08.Rep1: mtry= 9 
## - Fold08.Rep1: mtry= 9 
## + Fold08.Rep1: mtry= 6 
## - Fold08.Rep1: mtry= 6 
## + Fold08.Rep1: mtry=16 
## - Fold08.Rep1: mtry=16 
## + Fold08.Rep1: mtry= 4 
## - Fold08.Rep1: mtry= 4 
## + Fold09.Rep1: mtry=26 
## - Fold09.Rep1: mtry=26 
## + Fold09.Rep1: mtry=22 
## - Fold09.Rep1: mtry=22 
## + Fold09.Rep1: mtry= 5 
## - Fold09.Rep1: mtry= 5 
## + Fold09.Rep1: mtry=12 
## - Fold09.Rep1: mtry=12 
## + Fold09.Rep1: mtry=15 
## - Fold09.Rep1: mtry=15 
## + Fold09.Rep1: mtry= 9 
## - Fold09.Rep1: mtry= 9 
## + Fold09.Rep1: mtry= 6 
## - Fold09.Rep1: mtry= 6 
## + Fold09.Rep1: mtry=16 
## - Fold09.Rep1: mtry=16 
## + Fold09.Rep1: mtry= 4 
## - Fold09.Rep1: mtry= 4 
## + Fold10.Rep1: mtry=26 
## - Fold10.Rep1: mtry=26 
## + Fold10.Rep1: mtry=22 
## - Fold10.Rep1: mtry=22 
## + Fold10.Rep1: mtry= 5 
## - Fold10.Rep1: mtry= 5 
## + Fold10.Rep1: mtry=12 
## - Fold10.Rep1: mtry=12 
## + Fold10.Rep1: mtry=15 
## - Fold10.Rep1: mtry=15 
## + Fold10.Rep1: mtry= 9 
## - Fold10.Rep1: mtry= 9 
## + Fold10.Rep1: mtry= 6 
## - Fold10.Rep1: mtry= 6 
## + Fold10.Rep1: mtry=16 
## - Fold10.Rep1: mtry=16 
## + Fold10.Rep1: mtry= 4 
## - Fold10.Rep1: mtry= 4 
## + Fold01.Rep2: mtry=26 
## - Fold01.Rep2: mtry=26 
## + Fold01.Rep2: mtry=22 
## - Fold01.Rep2: mtry=22 
## + Fold01.Rep2: mtry= 5 
## - Fold01.Rep2: mtry= 5 
## + Fold01.Rep2: mtry=12 
## - Fold01.Rep2: mtry=12 
## + Fold01.Rep2: mtry=15 
## - Fold01.Rep2: mtry=15 
## + Fold01.Rep2: mtry= 9 
## - Fold01.Rep2: mtry= 9 
## + Fold01.Rep2: mtry= 6 
## - Fold01.Rep2: mtry= 6 
## + Fold01.Rep2: mtry=16 
## - Fold01.Rep2: mtry=16 
## + Fold01.Rep2: mtry= 4 
## - Fold01.Rep2: mtry= 4 
## + Fold02.Rep2: mtry=26 
## - Fold02.Rep2: mtry=26 
## + Fold02.Rep2: mtry=22 
## - Fold02.Rep2: mtry=22 
## + Fold02.Rep2: mtry= 5 
## - Fold02.Rep2: mtry= 5 
## + Fold02.Rep2: mtry=12 
## - Fold02.Rep2: mtry=12 
## + Fold02.Rep2: mtry=15 
## - Fold02.Rep2: mtry=15 
## + Fold02.Rep2: mtry= 9 
## - Fold02.Rep2: mtry= 9 
## + Fold02.Rep2: mtry= 6 
## - Fold02.Rep2: mtry= 6 
## + Fold02.Rep2: mtry=16 
## - Fold02.Rep2: mtry=16 
## + Fold02.Rep2: mtry= 4 
## - Fold02.Rep2: mtry= 4 
## + Fold03.Rep2: mtry=26 
## - Fold03.Rep2: mtry=26 
## + Fold03.Rep2: mtry=22 
## - Fold03.Rep2: mtry=22 
## + Fold03.Rep2: mtry= 5 
## - Fold03.Rep2: mtry= 5 
## + Fold03.Rep2: mtry=12 
## - Fold03.Rep2: mtry=12 
## + Fold03.Rep2: mtry=15 
## - Fold03.Rep2: mtry=15 
## + Fold03.Rep2: mtry= 9 
## - Fold03.Rep2: mtry= 9 
## + Fold03.Rep2: mtry= 6 
## - Fold03.Rep2: mtry= 6 
## + Fold03.Rep2: mtry=16 
## - Fold03.Rep2: mtry=16 
## + Fold03.Rep2: mtry= 4 
## - Fold03.Rep2: mtry= 4 
## + Fold04.Rep2: mtry=26 
## - Fold04.Rep2: mtry=26 
## + Fold04.Rep2: mtry=22 
## - Fold04.Rep2: mtry=22 
## + Fold04.Rep2: mtry= 5 
## - Fold04.Rep2: mtry= 5 
## + Fold04.Rep2: mtry=12 
## - Fold04.Rep2: mtry=12 
## + Fold04.Rep2: mtry=15 
## - Fold04.Rep2: mtry=15 
## + Fold04.Rep2: mtry= 9 
## - Fold04.Rep2: mtry= 9 
## + Fold04.Rep2: mtry= 6 
## - Fold04.Rep2: mtry= 6 
## + Fold04.Rep2: mtry=16 
## - Fold04.Rep2: mtry=16 
## + Fold04.Rep2: mtry= 4 
## - Fold04.Rep2: mtry= 4 
## + Fold05.Rep2: mtry=26 
## - Fold05.Rep2: mtry=26 
## + Fold05.Rep2: mtry=22 
## - Fold05.Rep2: mtry=22 
## + Fold05.Rep2: mtry= 5 
## - Fold05.Rep2: mtry= 5 
## + Fold05.Rep2: mtry=12 
## - Fold05.Rep2: mtry=12 
## + Fold05.Rep2: mtry=15 
## - Fold05.Rep2: mtry=15 
## + Fold05.Rep2: mtry= 9 
## - Fold05.Rep2: mtry= 9 
## + Fold05.Rep2: mtry= 6 
## - Fold05.Rep2: mtry= 6 
## + Fold05.Rep2: mtry=16 
## - Fold05.Rep2: mtry=16 
## + Fold05.Rep2: mtry= 4 
## - Fold05.Rep2: mtry= 4 
## + Fold06.Rep2: mtry=26 
## - Fold06.Rep2: mtry=26 
## + Fold06.Rep2: mtry=22 
## - Fold06.Rep2: mtry=22 
## + Fold06.Rep2: mtry= 5 
## - Fold06.Rep2: mtry= 5 
## + Fold06.Rep2: mtry=12 
## - Fold06.Rep2: mtry=12 
## + Fold06.Rep2: mtry=15 
## - Fold06.Rep2: mtry=15 
## + Fold06.Rep2: mtry= 9 
## - Fold06.Rep2: mtry= 9 
## + Fold06.Rep2: mtry= 6 
## - Fold06.Rep2: mtry= 6 
## + Fold06.Rep2: mtry=16 
## - Fold06.Rep2: mtry=16 
## + Fold06.Rep2: mtry= 4 
## - Fold06.Rep2: mtry= 4 
## + Fold07.Rep2: mtry=26 
## - Fold07.Rep2: mtry=26 
## + Fold07.Rep2: mtry=22 
## - Fold07.Rep2: mtry=22 
## + Fold07.Rep2: mtry= 5 
## - Fold07.Rep2: mtry= 5 
## + Fold07.Rep2: mtry=12 
## - Fold07.Rep2: mtry=12 
## + Fold07.Rep2: mtry=15 
## - Fold07.Rep2: mtry=15 
## + Fold07.Rep2: mtry= 9 
## - Fold07.Rep2: mtry= 9 
## + Fold07.Rep2: mtry= 6 
## - Fold07.Rep2: mtry= 6 
## + Fold07.Rep2: mtry=16 
## - Fold07.Rep2: mtry=16 
## + Fold07.Rep2: mtry= 4 
## - Fold07.Rep2: mtry= 4 
## + Fold08.Rep2: mtry=26 
## - Fold08.Rep2: mtry=26 
## + Fold08.Rep2: mtry=22 
## - Fold08.Rep2: mtry=22 
## + Fold08.Rep2: mtry= 5 
## - Fold08.Rep2: mtry= 5 
## + Fold08.Rep2: mtry=12 
## - Fold08.Rep2: mtry=12 
## + Fold08.Rep2: mtry=15 
## - Fold08.Rep2: mtry=15 
## + Fold08.Rep2: mtry= 9 
## - Fold08.Rep2: mtry= 9 
## + Fold08.Rep2: mtry= 6 
## - Fold08.Rep2: mtry= 6 
## + Fold08.Rep2: mtry=16 
## - Fold08.Rep2: mtry=16 
## + Fold08.Rep2: mtry= 4 
## - Fold08.Rep2: mtry= 4 
## + Fold09.Rep2: mtry=26 
## - Fold09.Rep2: mtry=26 
## + Fold09.Rep2: mtry=22 
## - Fold09.Rep2: mtry=22 
## + Fold09.Rep2: mtry= 5 
## - Fold09.Rep2: mtry= 5 
## + Fold09.Rep2: mtry=12 
## - Fold09.Rep2: mtry=12 
## + Fold09.Rep2: mtry=15 
## - Fold09.Rep2: mtry=15 
## + Fold09.Rep2: mtry= 9 
## - Fold09.Rep2: mtry= 9 
## + Fold09.Rep2: mtry= 6 
## - Fold09.Rep2: mtry= 6 
## + Fold09.Rep2: mtry=16 
## - Fold09.Rep2: mtry=16 
## + Fold09.Rep2: mtry= 4 
## - Fold09.Rep2: mtry= 4 
## + Fold10.Rep2: mtry=26 
## - Fold10.Rep2: mtry=26 
## + Fold10.Rep2: mtry=22 
## - Fold10.Rep2: mtry=22 
## + Fold10.Rep2: mtry= 5 
## - Fold10.Rep2: mtry= 5 
## + Fold10.Rep2: mtry=12 
## - Fold10.Rep2: mtry=12 
## + Fold10.Rep2: mtry=15 
## - Fold10.Rep2: mtry=15 
## + Fold10.Rep2: mtry= 9 
## - Fold10.Rep2: mtry= 9 
## + Fold10.Rep2: mtry= 6 
## - Fold10.Rep2: mtry= 6 
## + Fold10.Rep2: mtry=16 
## - Fold10.Rep2: mtry=16 
## + Fold10.Rep2: mtry= 4 
## - Fold10.Rep2: mtry= 4 
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 9 on full training set

model_rf_best$bestTune

confusion_matrix1<-caret::confusionMatrix(data = predict(model_rf_best,
                               data_valid),
                data_valid$y,
                positive = "FAIL")
Accuracy<-confusion_matrix[["overall"]]["Accuracy"] #Penghitungan Accuracy
Precision<-confusion_matrix[["byClass"]]["Pos Pred Value"] #Penghitungan Presisi
Recall<-confusion_matrix[["byClass"]]["Sensitivity"] #Penghitungan Recall/Sensitivity
F1_Score3<-(2*Precision*Recall)/(Precision+Recall) #Penghitungan F1 Score
data_metric_rf_best<-data.frame(Metric=c("Akurasi","Presisi","Recall","F1_Score"),Nilai=c(Accuracy,Precision,Recall,F1_Score3))
data_metric_rf_best

plot(varImp(model_rf),main="Variable Importance with Random Forest")

varImp(model_rf_best)

## rf variable importance
## 
##   only 20 most important variables shown (out of 26)
## 
##               Overall
## G2            100.000
## G1             47.059
## absences        8.695
## age             6.603
## failures2       3.384
## Fjobother       3.316
## famsizeLE3      2.757
## famrel4         2.390
## schoolsupyes    2.372
## activitiesyes   1.938
## Fedu3           1.917
## failures3       1.878
## Fedu2           1.850
## famrel5         1.761
## Fedu1           1.661
## goout5          1.623
## Fjobservices    1.621
## failures1       1.575
## goout3          1.448
## goout2          1.423

variabel<-c("G2","G1","absences","age","Fjob","famsize","famrel","failures","fedu","goout","activities","schoolsup")
importance<-c(100,34.56,3.9,3.83,2.96,1.51,1.45,1.39,1.21,1.08,1.05,1.04)
dataimportance<-data.frame(variabel,importance)
ggplot(dataimportance)+
  geom_col(aes(x=importance,y=fct_reorder(variabel,importance),fill=importance))+labs(title = "Variable Important Plot",y="Variabel")+scale_color_manual(values = "blue")

5. CONFUSSION MATRIX BEST MODEL

cm <- confusion_matrix1
confusion_matrix1$table

##           Reference
## Prediction FAIL PASS
##       FAIL   13    3
##       PASS    0   23

plt <- as.data.frame(cm$table)
plt$Prediction <- factor(plt$Prediction, levels=rev(levels(plt$Prediction)))

ggplot(plt, aes(Reference,Prediction, fill= Freq)) +
        geom_tile() + geom_text(aes(label=Freq)) +
        scale_fill_gradient(low="white", high="#009194") +
        labs(title="Confussion Matrix Best Model Random Forest",x = "Reference",y = "Prediction") +
        scale_x_discrete(labels=c("FAIL","PASS")) +
        scale_y_discrete(labels=c("PASS","FAIL"))+
  theme(plot.title = element_text(face="bold", size=18))

KARAKTERISTIK VARIABEL VARIABEL BERPENGARUH KATEGORIK TERHADAP G3 (KLASIFIKASI)

counts1 <- table(df$y, df$Fjob)
barplot(counts1, main="G3 berdasarkan Pekerjaan Ayah",
        xlab="Pekerjaan Ayah", col=c("#003333","#FF99FF"),
        legend = rownames(counts1))

counts2 <- table(df$y, df$famsize)
barplot(counts2, main="G3 berdasarkan Jumlah Anggota Rumah Tangga",
        xlab="Jumlah Anggota Rumah Tangga", col=c("#17594A","#B31312"),
        legend = rownames(counts2))

counts3 <- table(df$y, df$famrel)
barplot(counts3, main="G3 berdasarkan Hubungan dalam Keluarga",
        xlab="Hubungan dalam keluarga", col=c("#17594A","#B31312"),
        legend = rownames(counts3))

counts4 <- table(df$y, df$Fedu)
barplot(counts4, main="G3 berdasarkan Pendidikan Ayah",
        xlab="Pendidikan Ayah", col=c("#003333","#FF99FF"),
        legend = rownames(counts4))

counts5 <- table(df$y, df$goout)
barplot(counts5, main="G3 berdasarkan Intensitas Bermain Dengan Teman",
        xlab="Intensitas Bermain Dengan Teman", col=c("#FF2171","#F86F03"),
        legend = rownames(counts5))

counts6 <- table(df$y, df$activities)
barplot(counts6, main="G3 berdasarkan Keikutsertaan Ekstrakulikuler",
        xlab="Keikutsertaan Ekstrakulikuler", col=c("#FF2171","#F86F03"),
        legend = rownames(counts6))

counts7 <- table(df$y, df$schoolsup)
barplot(counts7, main="G3 berdasarkan Keikutsertaan Kegiatan Pembelajaran Tambahan",
        xlab="Keikutsertaan Kegiatan Pembelajaran Tambahan", col=c("#FF2171","#F86F03"),
        legend = rownames(counts7))

KARAKTERISTIK VARIABEL VARIABEL BEREPNGARUH NUMERIK TERHADAP G3 (KLASIFIKASI)

ggplot(data=df, aes(x=age, group=y, fill=y)) +
    geom_density(adjust=1.5, alpha=.4) +
    theme_ipsum()+labs(title = "Hubungan Umur Siswa dengan Capaian Belajar Matematika (G3)")

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
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## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

ggplot(data=df, aes(x=absences, group=y, fill=y)) +
    geom_density(adjust=1.5, alpha=.4) +
    theme_ipsum()+labs(title = "Hubungan Ketidakhadiran dengan Capaian Belajar Matematika (G3)")

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

ggplot(data=df, aes(x=failures, group=y, fill=y)) +
    geom_density(adjust=1.5, alpha=.4) +
    theme_ipsum()+labs(title = "Hubungan Tinggal Kelas dengan Capaian Belajar Matematika (G3)")

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

ggplot(data=df, aes(x=G1, group=y, fill=y)) +
    geom_density(adjust=1.5, alpha=.4) +
    theme_ipsum()+labs(title = "Hubungan G1 dengan Capaian Belajar Matematika (G3)")+
  scale_fill_manual(values=c("#898121","#525FE1"))

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

ggplot(data=df, aes(x=G2, group=y, fill=y)) +
    geom_density(adjust=1.5, alpha=.4) +
    theme_ipsum()+labs(title = "Hubungan G2 dengan Capaian Belajar Matematika (G3)")+
    scale_fill_manual(values=c("#898121","#525FE1"))

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database