ANLY 530 Project
Xue, Yingwen
2020-06-19
Title: Lab1 Method #1. Tree-based classification # Dataset:
# replace the ? (question marks) into NAs
rawdata <- read.csv(file="F:/homework/530/credit.csv", na.strings='?')
str(rawdata)## 'data.frame': 1000 obs. of 21 variables:
## $ Creditability : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Account.Balance : int 1 1 2 1 1 1 1 1 4 2 ...
## $ Duration.of.Credit..month. : int 18 9 12 12 12 10 8 6 18 24 ...
## $ Payment.Status.of.Previous.Credit: int 4 4 2 4 4 4 4 4 4 2 ...
## $ Purpose : int 2 0 9 0 0 0 0 0 3 3 ...
## $ Credit.Amount : int 1049 2799 841 2122 2171 2241 3398 1361 1098 3758 ...
## $ Value.Savings.Stocks : int 1 1 2 1 1 1 1 1 1 3 ...
## $ Length.of.current.employment : int 2 3 4 3 3 2 4 2 1 1 ...
## $ Instalment.per.cent : int 4 2 2 3 4 1 1 2 4 1 ...
## $ Sex...Marital.Status : int 2 3 2 3 3 3 3 3 2 2 ...
## $ Guarantors : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Duration.in.Current.address : int 4 2 4 2 4 3 4 4 4 4 ...
## $ Most.valuable.available.asset : int 2 1 1 1 2 1 1 1 3 4 ...
## $ Age..years. : int 21 36 23 39 38 48 39 40 65 23 ...
## $ Concurrent.Credits : int 3 3 3 3 1 3 3 3 3 3 ...
## $ Type.of.apartment : int 1 1 1 1 2 1 2 2 2 1 ...
## $ No.of.Credits.at.this.Bank : int 1 2 1 2 2 2 2 1 2 1 ...
## $ Occupation : int 3 3 2 2 2 2 2 2 1 1 ...
## $ No.of.dependents : int 1 2 1 2 1 2 1 2 1 1 ...
## $ Telephone : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Foreign.Worker : int 1 1 1 2 2 2 2 2 1 1 ...summary(rawdata)## Creditability Account.Balance Duration.of.Credit..month.
## Min. :0.0 Min. :1.000 Min. : 4.0
## 1st Qu.:0.0 1st Qu.:1.000 1st Qu.:12.0
## Median :1.0 Median :2.000 Median :18.0
## Mean :0.7 Mean :2.577 Mean :20.9
## 3rd Qu.:1.0 3rd Qu.:4.000 3rd Qu.:24.0
## Max. :1.0 Max. :4.000 Max. :72.0
## Payment.Status.of.Previous.Credit Purpose Credit.Amount
## Min. :0.000 Min. : 0.000 Min. : 250
## 1st Qu.:2.000 1st Qu.: 1.000 1st Qu.: 1366
## Median :2.000 Median : 2.000 Median : 2320
## Mean :2.545 Mean : 2.828 Mean : 3271
## 3rd Qu.:4.000 3rd Qu.: 3.000 3rd Qu.: 3972
## Max. :4.000 Max. :10.000 Max. :18424
## Value.Savings.Stocks Length.of.current.employment Instalment.per.cent
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:2.000
## Median :1.000 Median :3.000 Median :3.000
## Mean :2.105 Mean :3.384 Mean :2.973
## 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :4.000
## Sex...Marital.Status Guarantors Duration.in.Current.address
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.000 Median :3.000
## Mean :2.682 Mean :1.145 Mean :2.845
## 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:4.000
## Max. :4.000 Max. :3.000 Max. :4.000
## Most.valuable.available.asset Age..years. Concurrent.Credits
## Min. :1.000 Min. :19.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:27.00 1st Qu.:3.000
## Median :2.000 Median :33.00 Median :3.000
## Mean :2.358 Mean :35.54 Mean :2.675
## 3rd Qu.:3.000 3rd Qu.:42.00 3rd Qu.:3.000
## Max. :4.000 Max. :75.00 Max. :3.000
## Type.of.apartment No.of.Credits.at.this.Bank Occupation
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:3.000
## Median :2.000 Median :1.000 Median :3.000
## Mean :1.928 Mean :1.407 Mean :2.904
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :3.000 Max. :4.000 Max. :4.000
## No.of.dependents Telephone Foreign.Worker
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000
## Mean :1.155 Mean :1.404 Mean :1.037
## 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :2.000 Max. :2.000 Max. :2.000EDA:
#check the target variable"Creditability"
table(rawdata$Creditability)##
## 0 1
## 300 700#randomize the data
set.seed(12346)
#split randomized data to train and test sets
dateset <- rawdata[order(runif(1000)), ]
summary(dateset$Credit.Amount)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 250 1366 2320 3271 3972 18424train <- dateset[1:900, ]
test <- dateset[901:1000, ]
prop.table(table(train$Creditability))##
## 0 1
## 0.2955556 0.7044444Modelling
if (!require("C50")) {
install.packages("C50")
library(C50)
}## Loading required package: C50## Warning: package 'C50' was built under R version 3.6.3train$Creditability <- as.factor(train$Creditability)
test$Creditability <- as.factor(test$Creditability)
credit_model <- C5.0(x = train[-1], y = train$Creditability)
summary(credit_model)##
## Call:
## C5.0.default(x = train[-1], y = train$Creditability)
##
##
## C5.0 [Release 2.07 GPL Edition] Fri Jun 19 18:29:01 2020
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 900 cases (21 attributes) from undefined.data
##
## Decision tree:
##
## Account.Balance > 2:
## :...Account.Balance <= 3:
## : :...Foreign.Worker > 1: 1 (3)
## : : Foreign.Worker <= 1:
## : : :...Most.valuable.available.asset <= 1:
## : : :...Occupation <= 2: 0 (5)
## : : : Occupation > 2:
## : : : :...Instalment.per.cent <= 3: 1 (7)
## : : : Instalment.per.cent > 3: 0 (4/1)
## : : Most.valuable.available.asset > 1:
## : : :...No.of.dependents <= 1: 1 (34/4)
## : : No.of.dependents > 1:
## : : :...Credit.Amount <= 1278: 0 (2)
## : : Credit.Amount > 1278: 1 (2)
## : Account.Balance > 3:
## : :...Concurrent.Credits <= 2:
## : :...Purpose <= 0:
## : : :...No.of.Credits.at.this.Bank <= 1: 1 (3)
## : : : No.of.Credits.at.this.Bank > 1: 0 (6/1)
## : : Purpose > 0:
## : : :...Purpose <= 4: 1 (28/2)
## : : Purpose > 4:
## : : :...Instalment.per.cent <= 1: 1 (2)
## : : Instalment.per.cent > 1:
## : : :...Length.of.current.employment <= 3: 0 (6/1)
## : : Length.of.current.employment > 3: 1 (7/1)
## : Concurrent.Credits > 2:
## : :...Length.of.current.employment > 3: 1 (139/5)
## : Length.of.current.employment <= 3:
## : :...Credit.Amount <= 4455: 1 (134/11)
## : Credit.Amount > 4455:
## : :...No.of.dependents > 1: 1 (5)
## : No.of.dependents <= 1:
## : :...Purpose <= 1:
## : :...Duration.of.Credit..month. <= 42: 1 (9)
## : : Duration.of.Credit..month. > 42: 0 (2)
## : Purpose > 1:
## : :...Sex...Marital.Status <= 2: 0 (4)
## : Sex...Marital.Status > 2:
## : :...Occupation <= 3: 1 (2)
## : Occupation > 3: 0 (4)
## Account.Balance <= 2:
## :...Payment.Status.of.Previous.Credit <= 1:
## :...Type.of.apartment <= 1: 0 (14/1)
## : Type.of.apartment > 1:
## : :...Type.of.apartment > 2:
## : :...Length.of.current.employment <= 2: 1 (3/1)
## : : Length.of.current.employment > 2: 0 (8)
## : Type.of.apartment <= 2:
## : :...Occupation > 3: 0 (3)
## : Occupation <= 3:
## : :...Age..years. > 35: 0 (9/1)
## : Age..years. <= 35:
## : :...Telephone > 1: 1 (8/1)
## : Telephone <= 1:
## : :...Credit.Amount <= 3556: 0 (8/2)
## : Credit.Amount > 3556: 1 (5)
## Payment.Status.of.Previous.Credit > 1:
## :...Duration.of.Credit..month. > 22:
## :...Value.Savings.Stocks > 3:
## : :...Account.Balance > 1: 1 (19/1)
## : : Account.Balance <= 1:
## : : :...No.of.Credits.at.this.Bank > 1: 1 (3)
## : : No.of.Credits.at.this.Bank <= 1:
## : : :...Occupation > 3: 0 (2)
## : : Occupation <= 3:
## : : :...Telephone <= 1: 0 (7/1)
## : : Telephone > 1: 1 (2)
## : Value.Savings.Stocks <= 3:
## : :...Duration.of.Credit..month. > 42:
## : :...Duration.in.Current.address <= 1: 1 (3/1)
## : : Duration.in.Current.address > 1: 0 (25/2)
## : Duration.of.Credit..month. <= 42:
## : :...Purpose <= 0:
## : :...Value.Savings.Stocks > 1: 1 (4/1)
## : : Value.Savings.Stocks <= 1:
## : : :...Instalment.per.cent > 2: 0 (18/1)
## : : Instalment.per.cent <= 2:
## : : :...Age..years. <= 35: 0 (2)
## : : Age..years. > 35: 1 (2)
## : Purpose > 0:
## : :...Age..years. > 52: 1 (7)
## : Age..years. <= 52:
## : :...Type.of.apartment > 2:
## : :...Age..years. <= 46: 1 (8)
## : : Age..years. > 46: 0 (2)
## : Type.of.apartment <= 2:
## : :...Concurrent.Credits <= 1: 0 (8/1)
## : Concurrent.Credits > 1:
## : :...Sex...Marital.Status <= 2: [S1]
## : Sex...Marital.Status > 2:
## : :...No.of.Credits.at.this.Bank > 1:
## : :...Age..years. <= 25: 0 (5/1)
## : : Age..years. > 25: 1 (12/1)
## : No.of.Credits.at.this.Bank <= 1:
## : :...Guarantors > 2: 1 (2)
## : Guarantors <= 2:
## : :...No.of.dependents > 1: 0 (5)
## : No.of.dependents <= 1:
## : :...Telephone <= 1: [S2]
## : Telephone > 1: [S3]
## Duration.of.Credit..month. <= 22:
## :...Guarantors > 2:
## :...Purpose <= 1: 0 (4/1)
## : Purpose > 1: 1 (24)
## Guarantors <= 2:
## :...Payment.Status.of.Previous.Credit > 3: 1 (67/13)
## Payment.Status.of.Previous.Credit <= 3:
## :...Value.Savings.Stocks > 2: 1 (44/10)
## Value.Savings.Stocks <= 2:
## :...Instalment.per.cent <= 3:
## :...Occupation <= 2: 1 (16/2)
## : Occupation > 2:
## : :...No.of.Credits.at.this.Bank > 1:
## : :...Account.Balance <= 1: 0 (2)
## : : Account.Balance > 1:
## : : :...Type.of.apartment <= 1: 0 (2)
## : : Type.of.apartment > 1: 1 (3)
## : No.of.Credits.at.this.Bank <= 1:
## : :...Telephone > 1: [S4]
## : Telephone <= 1:
## : :...Credit.Amount <= 1347: 0 (6)
## : Credit.Amount > 1347: [S5]
## Instalment.per.cent > 3:
## :...No.of.Credits.at.this.Bank > 1:
## :...Type.of.apartment <= 1: 0 (3)
## : Type.of.apartment > 1: 1 (5)
## No.of.Credits.at.this.Bank <= 1:
## :...Age..years. > 35: 0 (11/1)
## Age..years. <= 35:
## :...Length.of.current.employment <= 1: 0 (4)
## Length.of.current.employment > 1:
## :...Credit.Amount > 1755: 1 (7)
## Credit.Amount <= 1755:
## :...Value.Savings.Stocks > 1: 0 (2)
## Value.Savings.Stocks <= 1:
## :...Telephone > 1: 0 (2)
## Telephone <= 1: [S6]
##
## SubTree [S1]
##
## No.of.Credits.at.this.Bank > 1: 0 (4)
## No.of.Credits.at.this.Bank <= 1:
## :...Duration.of.Credit..month. > 33: 1 (2)
## Duration.of.Credit..month. <= 33:
## :...Purpose <= 2: 0 (8/1)
## Purpose > 2:
## :...Account.Balance <= 1: 0 (5/1)
## Account.Balance > 1: 1 (3)
##
## SubTree [S2]
##
## Age..years. <= 31: 1 (8)
## Age..years. > 31: 0 (4/1)
##
## SubTree [S3]
##
## Age..years. <= 29: 0 (5)
## Age..years. > 29: 1 (5/1)
##
## SubTree [S4]
##
## Most.valuable.available.asset <= 3: 1 (10/1)
## Most.valuable.available.asset > 3: 0 (5/1)
##
## SubTree [S5]
##
## Duration.of.Credit..month. > 16: 0 (4/1)
## Duration.of.Credit..month. <= 16:
## :...Purpose <= 2: 1 (11)
## Purpose > 2:
## :...Length.of.current.employment <= 3: 0 (2)
## Length.of.current.employment > 3: 1 (3)
##
## SubTree [S6]
##
## Length.of.current.employment > 4: 1 (3)
## Length.of.current.employment <= 4:
## :...Length.of.current.employment > 3: 0 (2)
## Length.of.current.employment <= 3:
## :...Age..years. <= 27: 1 (9/3)
## Age..years. > 27: 0 (3)
##
##
## Evaluation on training data (900 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 83 79( 8.8%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 207 59 (a): class 0
## 20 614 (b): class 1
##
##
## Attribute usage:
##
## 100.00% Account.Balance
## 54.67% Payment.Status.of.Previous.Credit
## 49.44% Duration.of.Credit..month.
## 47.44% Concurrent.Credits
## 40.00% Length.of.current.employment
## 37.67% Value.Savings.Stocks
## 31.44% Guarantors
## 26.22% Purpose
## 25.67% Credit.Amount
## 21.11% No.of.Credits.at.this.Bank
## 18.89% Age..years.
## 18.11% Instalment.per.cent
## 17.44% Type.of.apartment
## 14.44% Occupation
## 12.44% Telephone
## 10.11% No.of.dependents
## 8.67% Sex...Marital.Status
## 7.67% Most.valuable.available.asset
## 6.33% Foreign.Worker
## 3.11% Duration.in.Current.address
##
##
## Time: 0.0 secsEvaluate the model
cred_pred <- predict(credit_model, test)
#Model Evaluation Method 1
if (!require("gmodels")) {
install.packages("gmodels")
library(gmodels)
}## Loading required package: gmodels## Warning: package 'gmodels' was built under R version 3.6.2CrossTable(test$Creditability, cred_pred, prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE, dnn = c('Actual Creditability', 'Predicted Creditability'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 100
##
##
## | Predicted Creditability
## Actual Creditability | 0 | 1 | Row Total |
## ---------------------|-----------|-----------|-----------|
## 0 | 19 | 15 | 34 |
## | 0.190 | 0.150 | |
## ---------------------|-----------|-----------|-----------|
## 1 | 9 | 57 | 66 |
## | 0.090 | 0.570 | |
## ---------------------|-----------|-----------|-----------|
## Column Total | 28 | 72 | 100 |
## ---------------------|-----------|-----------|-----------|
##
## (p <- table(cred_pred, test$Creditability))##
## cred_pred 0 1
## 0 19 9
## 1 15 57#Accuracy
(Accuracy <- sum(diag(p))/sum(p)*100)## [1] 76Q1- If you see the accuracy 0f 100%, what does it mean? Does this mean that we design a perfect
model? This is some thing that needs more discussion. Write a few sentences about accuracy of
100%.
Ans:Accurary used to measure how training model matchs test dataset.We cant conclude the model is perfact because it might be overfitted.We need use combination of accuracy and other parameter to prove the performance of our training model.
Alternative solution & Visualize the model
if (!require("rpart")) {
install.packages("rpart")
library(rpart)
}## Loading required package: rpartif (!require("rpart.plot")) {
install.packages("rpart.plot")
library(rpart.plot)
}## Loading required package: rpart.plot## Warning: package 'rpart.plot' was built under R version 3.6.3if (!require("ggplot2")) {
install.packages("ggplot2")
library(ggplot2)
}## Loading required package: ggplot2DT <- rpart(Creditability ~ . , data= train)
summary(DT)## Call:
## rpart(formula = Creditability ~ ., data = train)
## n= 900
##
## CP nsplit rel error xerror xstd
## 1 0.04323308 0 1.0000000 1.0000000 0.05146151
## 2 0.04135338 3 0.8496241 0.9812030 0.05117617
## 3 0.01629073 4 0.8082707 0.9398496 0.05051540
## 4 0.01315789 7 0.7593985 0.9661654 0.05094120
## 5 0.01127820 14 0.6654135 0.9774436 0.05111799
## 6 0.01000000 15 0.6541353 0.9736842 0.05105944
##
## Variable importance
## Account.Balance Duration.of.Credit..month.
## 30 17
## Credit.Amount Value.Savings.Stocks
## 11 10
## Payment.Status.of.Previous.Credit Purpose
## 9 5
## Most.valuable.available.asset Age..years.
## 4 3
## Length.of.current.employment Guarantors
## 3 3
## Concurrent.Credits Occupation
## 2 2
## Type.of.apartment
## 1
##
## Node number 1: 900 observations, complexity param=0.04323308
## predicted class=1 expected loss=0.2955556 P(node) =1
## class counts: 266 634
## probabilities: 0.296 0.704
## left son=2 (492 obs) right son=3 (408 obs)
## Primary splits:
## Account.Balance < 2.5 to the left, improve=40.96088, (0 missing)
## Payment.Status.of.Previous.Credit < 1.5 to the left, improve=15.92438, (0 missing)
## Value.Savings.Stocks < 2.5 to the left, improve=12.98196, (0 missing)
## Duration.of.Credit..month. < 43.5 to the right, improve=12.49926, (0 missing)
## Credit.Amount < 11191 to the right, improve=11.19833, (0 missing)
## Surrogate splits:
## Value.Savings.Stocks < 2.5 to the left, agree=0.617, adj=0.154, (0 split)
## Payment.Status.of.Previous.Credit < 3.5 to the left, agree=0.591, adj=0.098, (0 split)
## Age..years. < 30.5 to the left, agree=0.557, adj=0.022, (0 split)
## Duration.of.Credit..month. < 6.5 to the right, agree=0.554, adj=0.017, (0 split)
## Length.of.current.employment < 4.5 to the left, agree=0.554, adj=0.017, (0 split)
##
## Node number 2: 492 observations, complexity param=0.04323308
## predicted class=1 expected loss=0.4329268 P(node) =0.5466667
## class counts: 213 279
## probabilities: 0.433 0.567
## left son=4 (213 obs) right son=5 (279 obs)
## Primary splits:
## Duration.of.Credit..month. < 22.5 to the right, improve=11.010300, (0 missing)
## Payment.Status.of.Previous.Credit < 1.5 to the left, improve= 8.667244, (0 missing)
## Most.valuable.available.asset < 1.5 to the right, improve= 8.534391, (0 missing)
## Value.Savings.Stocks < 2.5 to the left, improve= 7.282673, (0 missing)
## Credit.Amount < 8079 to the right, improve= 7.238941, (0 missing)
## Surrogate splits:
## Credit.Amount < 2805.5 to the right, agree=0.756, adj=0.437, (0 split)
## Most.valuable.available.asset < 2.5 to the right, agree=0.652, adj=0.197, (0 split)
## Type.of.apartment < 2.5 to the right, agree=0.610, adj=0.099, (0 split)
## Purpose < 8.5 to the right, agree=0.608, adj=0.094, (0 split)
## Occupation < 3.5 to the right, agree=0.598, adj=0.070, (0 split)
##
## Node number 3: 408 observations
## predicted class=1 expected loss=0.129902 P(node) =0.4533333
## class counts: 53 355
## probabilities: 0.130 0.870
##
## Node number 4: 213 observations, complexity param=0.04323308
## predicted class=0 expected loss=0.4460094 P(node) =0.2366667
## class counts: 118 95
## probabilities: 0.554 0.446
## left son=8 (176 obs) right son=9 (37 obs)
## Primary splits:
## Value.Savings.Stocks < 3.5 to the left, improve=7.209076, (0 missing)
## Duration.of.Credit..month. < 43.5 to the right, improve=3.897888, (0 missing)
## Purpose < 0.5 to the left, improve=3.207167, (0 missing)
## Credit.Amount < 1376.5 to the left, improve=3.155186, (0 missing)
## Instalment.per.cent < 2.5 to the right, improve=2.469776, (0 missing)
##
## Node number 5: 279 observations, complexity param=0.04135338
## predicted class=1 expected loss=0.3405018 P(node) =0.31
## class counts: 95 184
## probabilities: 0.341 0.659
## left son=10 (25 obs) right son=11 (254 obs)
## Primary splits:
## Payment.Status.of.Previous.Credit < 1.5 to the left, improve=7.909699, (0 missing)
## Most.valuable.available.asset < 1.5 to the right, improve=5.732535, (0 missing)
## Credit.Amount < 1285 to the left, improve=3.493557, (0 missing)
## Guarantors < 2.5 to the left, improve=3.389748, (0 missing)
## Duration.of.Credit..month. < 11.5 to the right, improve=3.193548, (0 missing)
##
## Node number 8: 176 observations, complexity param=0.01315789
## predicted class=0 expected loss=0.3863636 P(node) =0.1955556
## class counts: 108 68
## probabilities: 0.614 0.386
## left son=16 (37 obs) right son=17 (139 obs)
## Primary splits:
## Duration.of.Credit..month. < 43.5 to the right, improve=5.913810, (0 missing)
## Instalment.per.cent < 2.5 to the right, improve=2.169142, (0 missing)
## Credit.Amount < 11788 to the right, improve=2.048485, (0 missing)
## Purpose < 0.5 to the left, improve=1.923477, (0 missing)
## Duration.in.Current.address < 1.5 to the right, improve=1.757062, (0 missing)
## Surrogate splits:
## Credit.Amount < 13319.5 to the right, agree=0.812, adj=0.108, (0 split)
##
## Node number 9: 37 observations
## predicted class=1 expected loss=0.2702703 P(node) =0.04111111
## class counts: 10 27
## probabilities: 0.270 0.730
##
## Node number 10: 25 observations
## predicted class=0 expected loss=0.28 P(node) =0.02777778
## class counts: 18 7
## probabilities: 0.720 0.280
##
## Node number 11: 254 observations, complexity param=0.01629073
## predicted class=1 expected loss=0.3031496 P(node) =0.2822222
## class counts: 77 177
## probabilities: 0.303 0.697
## left son=22 (150 obs) right son=23 (104 obs)
## Primary splits:
## Most.valuable.available.asset < 1.5 to the right, improve=3.609063, (0 missing)
## Duration.of.Credit..month. < 11.5 to the right, improve=3.068361, (0 missing)
## Credit.Amount < 1285 to the left, improve=3.034775, (0 missing)
## Guarantors < 2.5 to the left, improve=2.417995, (0 missing)
## Age..years. < 25.5 to the left, improve=1.818908, (0 missing)
## Surrogate splits:
## Occupation < 2.5 to the right, agree=0.661, adj=0.173, (0 split)
## Guarantors < 1.5 to the left, agree=0.646, adj=0.135, (0 split)
## Sex...Marital.Status < 3.5 to the left, agree=0.618, adj=0.067, (0 split)
## Duration.of.Credit..month. < 10.5 to the right, agree=0.614, adj=0.058, (0 split)
## Foreign.Worker < 1.5 to the left, agree=0.614, adj=0.058, (0 split)
##
## Node number 16: 37 observations
## predicted class=0 expected loss=0.1351351 P(node) =0.04111111
## class counts: 32 5
## probabilities: 0.865 0.135
##
## Node number 17: 139 observations, complexity param=0.01315789
## predicted class=0 expected loss=0.4532374 P(node) =0.1544444
## class counts: 76 63
## probabilities: 0.547 0.453
## left son=34 (28 obs) right son=35 (111 obs)
## Primary splits:
## Purpose < 0.5 to the left, improve=2.896591, (0 missing)
## Credit.Amount < 2249 to the left, improve=2.896591, (0 missing)
## Guarantors < 2.5 to the left, improve=2.591311, (0 missing)
## Instalment.per.cent < 2.5 to the right, improve=1.888214, (0 missing)
## Sex...Marital.Status < 2.5 to the left, improve=1.527897, (0 missing)
## Surrogate splits:
## Credit.Amount < 1368.5 to the left, agree=0.827, adj=0.143, (0 split)
##
## Node number 22: 150 observations, complexity param=0.01629073
## predicted class=1 expected loss=0.3733333 P(node) =0.1666667
## class counts: 56 94
## probabilities: 0.373 0.627
## left son=44 (49 obs) right son=45 (101 obs)
## Primary splits:
## Credit.Amount < 1285.5 to the left, improve=4.595234, (0 missing)
## Payment.Status.of.Previous.Credit < 3.5 to the left, improve=2.389231, (0 missing)
## No.of.Credits.at.this.Bank < 1.5 to the left, improve=1.698487, (0 missing)
## Age..years. < 25.5 to the left, improve=1.366154, (0 missing)
## Duration.of.Credit..month. < 8.5 to the right, improve=1.153320, (0 missing)
## Surrogate splits:
## Duration.of.Credit..month. < 8.5 to the left, agree=0.707, adj=0.102, (0 split)
## Purpose < 3.5 to the right, agree=0.700, adj=0.082, (0 split)
## Age..years. < 20.5 to the left, agree=0.687, adj=0.041, (0 split)
## No.of.dependents < 1.5 to the right, agree=0.680, adj=0.020, (0 split)
##
## Node number 23: 104 observations
## predicted class=1 expected loss=0.2019231 P(node) =0.1155556
## class counts: 21 83
## probabilities: 0.202 0.798
##
## Node number 34: 28 observations
## predicted class=0 expected loss=0.25 P(node) =0.03111111
## class counts: 21 7
## probabilities: 0.750 0.250
##
## Node number 35: 111 observations, complexity param=0.01315789
## predicted class=1 expected loss=0.4954955 P(node) =0.1233333
## class counts: 55 56
## probabilities: 0.495 0.505
## left son=70 (102 obs) right son=71 (9 obs)
## Primary splits:
## Guarantors < 2.5 to the left, improve=2.894188, (0 missing)
## Purpose < 1.5 to the right, improve=2.474901, (0 missing)
## Age..years. < 52.5 to the left, improve=1.919258, (0 missing)
## Concurrent.Credits < 1.5 to the left, improve=1.632795, (0 missing)
## Sex...Marital.Status < 2.5 to the left, improve=1.424384, (0 missing)
##
## Node number 44: 49 observations, complexity param=0.01629073
## predicted class=0 expected loss=0.4489796 P(node) =0.05444444
## class counts: 27 22
## probabilities: 0.551 0.449
## left son=88 (37 obs) right son=89 (12 obs)
## Primary splits:
## Duration.of.Credit..month. < 8.5 to the right, improve=4.695348, (0 missing)
## Telephone < 1.5 to the left, improve=3.629513, (0 missing)
## Age..years. < 36.5 to the left, improve=3.482993, (0 missing)
## Most.valuable.available.asset < 2.5 to the right, improve=3.067640, (0 missing)
## Length.of.current.employment < 3.5 to the left, improve=2.803821, (0 missing)
## Surrogate splits:
## Credit.Amount < 624 to the right, agree=0.857, adj=0.417, (0 split)
## Occupation < 3.5 to the left, agree=0.796, adj=0.167, (0 split)
##
## Node number 45: 101 observations, complexity param=0.0112782
## predicted class=1 expected loss=0.2871287 P(node) =0.1122222
## class counts: 29 72
## probabilities: 0.287 0.713
## left son=90 (9 obs) right son=91 (92 obs)
## Primary splits:
## Credit.Amount < 6414.5 to the right, improve=2.846535, (0 missing)
## Length.of.current.employment < 4.5 to the right, improve=2.549066, (0 missing)
## Age..years. < 49 to the right, improve=1.884996, (0 missing)
## Payment.Status.of.Previous.Credit < 3.5 to the left, improve=1.604687, (0 missing)
## Instalment.per.cent < 3.5 to the left, improve=1.530175, (0 missing)
## Surrogate splits:
## Age..years. < 67.5 to the right, agree=0.921, adj=0.111, (0 split)
##
## Node number 70: 102 observations, complexity param=0.01315789
## predicted class=0 expected loss=0.4705882 P(node) =0.1133333
## class counts: 54 48
## probabilities: 0.529 0.471
## left son=140 (85 obs) right son=141 (17 obs)
## Primary splits:
## Purpose < 1.5 to the right, improve=2.258824, (0 missing)
## Concurrent.Credits < 1.5 to the left, improve=2.258824, (0 missing)
## Payment.Status.of.Previous.Credit < 1.5 to the left, improve=1.846785, (0 missing)
## Sex...Marital.Status < 2.5 to the left, improve=1.838022, (0 missing)
## Length.of.current.employment < 3.5 to the left, improve=1.411765, (0 missing)
##
## Node number 71: 9 observations
## predicted class=1 expected loss=0.1111111 P(node) =0.01
## class counts: 1 8
## probabilities: 0.111 0.889
##
## Node number 88: 37 observations
## predicted class=0 expected loss=0.3243243 P(node) =0.04111111
## class counts: 25 12
## probabilities: 0.676 0.324
##
## Node number 89: 12 observations
## predicted class=1 expected loss=0.1666667 P(node) =0.01333333
## class counts: 2 10
## probabilities: 0.167 0.833
##
## Node number 90: 9 observations
## predicted class=0 expected loss=0.3333333 P(node) =0.01
## class counts: 6 3
## probabilities: 0.667 0.333
##
## Node number 91: 92 observations
## predicted class=1 expected loss=0.25 P(node) =0.1022222
## class counts: 23 69
## probabilities: 0.250 0.750
##
## Node number 140: 85 observations, complexity param=0.01315789
## predicted class=0 expected loss=0.4235294 P(node) =0.09444444
## class counts: 49 36
## probabilities: 0.576 0.424
## left son=280 (16 obs) right son=281 (69 obs)
## Primary splits:
## Concurrent.Credits < 1.5 to the left, improve=2.196100, (0 missing)
## Payment.Status.of.Previous.Credit < 1.5 to the left, improve=1.820168, (0 missing)
## Telephone < 1.5 to the right, improve=1.466993, (0 missing)
## Age..years. < 28.5 to the right, improve=1.191024, (0 missing)
## Duration.in.Current.address < 1.5 to the right, improve=1.152941, (0 missing)
## Surrogate splits:
## Duration.of.Credit..month. < 39.5 to the right, agree=0.824, adj=0.062, (0 split)
##
## Node number 141: 17 observations
## predicted class=1 expected loss=0.2941176 P(node) =0.01888889
## class counts: 5 12
## probabilities: 0.294 0.706
##
## Node number 280: 16 observations
## predicted class=0 expected loss=0.1875 P(node) =0.01777778
## class counts: 13 3
## probabilities: 0.812 0.188
##
## Node number 281: 69 observations, complexity param=0.01315789
## predicted class=0 expected loss=0.4782609 P(node) =0.07666667
## class counts: 36 33
## probabilities: 0.522 0.478
## left son=562 (44 obs) right son=563 (25 obs)
## Primary splits:
## Age..years. < 28.5 to the right, improve=2.0511460, (0 missing)
## Telephone < 1.5 to the right, improve=1.6213370, (0 missing)
## Length.of.current.employment < 3.5 to the left, improve=1.2326770, (0 missing)
## Credit.Amount < 2440.5 to the left, improve=0.8487091, (0 missing)
## Sex...Marital.Status < 2.5 to the left, improve=0.7317415, (0 missing)
## Surrogate splits:
## Value.Savings.Stocks < 2.5 to the left, agree=0.696, adj=0.16, (0 split)
## Most.valuable.available.asset < 1.5 to the right, agree=0.681, adj=0.12, (0 split)
## Sex...Marital.Status < 3.5 to the left, agree=0.667, adj=0.08, (0 split)
## Type.of.apartment < 1.5 to the right, agree=0.652, adj=0.04, (0 split)
##
## Node number 562: 44 observations, complexity param=0.01315789
## predicted class=0 expected loss=0.3863636 P(node) =0.04888889
## class counts: 27 17
## probabilities: 0.614 0.386
## left son=1124 (30 obs) right son=1125 (14 obs)
## Primary splits:
## Length.of.current.employment < 3.5 to the left, improve=2.7017320, (0 missing)
## Payment.Status.of.Previous.Credit < 2.5 to the left, improve=2.0774290, (0 missing)
## No.of.Credits.at.this.Bank < 1.5 to the left, improve=1.2803030, (0 missing)
## Sex...Marital.Status < 2.5 to the left, improve=0.9350649, (0 missing)
## Credit.Amount < 2168.5 to the left, improve=0.6096681, (0 missing)
## Surrogate splits:
## Age..years. < 52.5 to the left, agree=0.705, adj=0.071, (0 split)
##
## Node number 563: 25 observations
## predicted class=1 expected loss=0.36 P(node) =0.02777778
## class counts: 9 16
## probabilities: 0.360 0.640
##
## Node number 1124: 30 observations
## predicted class=0 expected loss=0.2666667 P(node) =0.03333333
## class counts: 22 8
## probabilities: 0.733 0.267
##
## Node number 1125: 14 observations
## predicted class=1 expected loss=0.3571429 P(node) =0.01555556
## class counts: 5 9
## probabilities: 0.357 0.643rpart.plot(DT, type=1, extra = 102)
#Method #2. Random forest
if (!require("randomForest")) {
install.packages("randomForest")
library(randomForest)
}## Loading required package: randomForest## Warning: package 'randomForest' was built under R version 3.6.3## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margintrain$Creditability <- as.factor(train$Creditability)
rf_model <- randomForest(Creditability ~ . , data= train)
summary(rf_model)## Length Class Mode
## call 3 -none- call
## type 1 -none- character
## predicted 900 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1800 matrix numeric
## oob.times 900 -none- numeric
## classes 2 -none- character
## importance 20 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 900 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call#Evaluation
cred_pred <- predict(rf_model, test)
(p <- table(cred_pred, test$Creditability))##
## cred_pred 0 1
## 0 17 10
## 1 17 56#Accuracy
(Accuracy <- sum(diag(p))/sum(p)*100)## [1] 73Q2- What are the three most important features in this model.
importance(rf_model)## MeanDecreaseGini
## Account.Balance 40.807660
## Duration.of.Credit..month. 38.138331
## Payment.Status.of.Previous.Credit 21.509260
## Purpose 23.149714
## Credit.Amount 49.940301
## Value.Savings.Stocks 17.648853
## Length.of.current.employment 18.605632
## Instalment.per.cent 15.410043
## Sex...Marital.Status 13.440547
## Guarantors 7.243751
## Duration.in.Current.address 15.275036
## Most.valuable.available.asset 16.931605
## Age..years. 39.749907
## Concurrent.Credits 8.609968
## Type.of.apartment 9.814678
## No.of.Credits.at.this.Bank 8.131338
## Occupation 11.930925
## No.of.dependents 5.113108
## Telephone 7.538531
## Foreign.Worker 1.513942#Three most imprtant features are Account.Balance at 49.280951, Account Balance at 41.392868, and Age by years at 38.919060.
#Now, Change the random seed to 23458 and find the new accuracy of random forest.
set.seed(23458)
rf_model1 <- randomForest(Creditability ~ (Credit.Amount + Account.Balance + Age..years.), data= train)
cred_pred1 <- predict(rf_model1, test)
(p1 <- table(cred_pred1, test$Creditability))##
## cred_pred1 0 1
## 0 8 7
## 1 26 59(Accuracy <- sum(diag(p1))/sum(p1)*100)## [1] 67#The accuracy is drop from 74 to 67 for this model when more parameters are added.Method #3. Adding regression to trees
#Loading the data
winedata <- read.csv(file="F:/homework/530/whitewines.csv", na.strings='?')
str(winedata)## 'data.frame': 4898 obs. of 12 variables:
## $ fixed.acidity : num 6.7 5.7 5.9 5.3 6.4 7 7.9 6.6 7 6.5 ...
## $ volatile.acidity : num 0.62 0.22 0.19 0.47 0.29 0.14 0.12 0.38 0.16 0.37 ...
## $ citric.acid : num 0.24 0.2 0.26 0.1 0.21 0.41 0.49 0.28 0.3 0.33 ...
## $ residual.sugar : num 1.1 16 7.4 1.3 9.65 0.9 5.2 2.8 2.6 3.9 ...
## $ chlorides : num 0.039 0.044 0.034 0.036 0.041 0.037 0.049 0.043 0.043 0.027 ...
## $ free.sulfur.dioxide : num 6 41 33 11 36 22 33 17 34 40 ...
## $ total.sulfur.dioxide: num 62 113 123 74 119 95 152 67 90 130 ...
## $ density : num 0.993 0.999 0.995 0.991 0.993 ...
## $ pH : num 3.41 3.22 3.49 3.48 2.99 3.25 3.18 3.21 2.88 3.28 ...
## $ sulphates : num 0.32 0.46 0.42 0.54 0.34 0.43 0.47 0.47 0.47 0.39 ...
## $ alcohol : num 10.4 8.9 10.1 11.2 10.9 ...
## $ quality : int 5 6 6 4 6 6 6 6 6 7 ...summary(winedata)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 3.800 Min. :0.0800 Min. :0.0000 Min. : 0.600
## 1st Qu.: 6.300 1st Qu.:0.2100 1st Qu.:0.2700 1st Qu.: 1.700
## Median : 6.800 Median :0.2600 Median :0.3200 Median : 5.200
## Mean : 6.855 Mean :0.2782 Mean :0.3342 Mean : 6.391
## 3rd Qu.: 7.300 3rd Qu.:0.3200 3rd Qu.:0.3900 3rd Qu.: 9.900
## Max. :14.200 Max. :1.1000 Max. :1.6600 Max. :65.800
## chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. :0.00900 Min. : 2.00 Min. : 9.0
## 1st Qu.:0.03600 1st Qu.: 23.00 1st Qu.:108.0
## Median :0.04300 Median : 34.00 Median :134.0
## Mean :0.04577 Mean : 35.31 Mean :138.4
## 3rd Qu.:0.05000 3rd Qu.: 46.00 3rd Qu.:167.0
## Max. :0.34600 Max. :289.00 Max. :440.0
## density pH sulphates alcohol
## Min. :0.9871 Min. :2.720 Min. :0.2200 Min. : 8.00
## 1st Qu.:0.9917 1st Qu.:3.090 1st Qu.:0.4100 1st Qu.: 9.50
## Median :0.9937 Median :3.180 Median :0.4700 Median :10.40
## Mean :0.9940 Mean :3.188 Mean :0.4898 Mean :10.51
## 3rd Qu.:0.9961 3rd Qu.:3.280 3rd Qu.:0.5500 3rd Qu.:11.40
## Max. :1.0390 Max. :3.820 Max. :1.0800 Max. :14.20
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.878
## 3rd Qu.:6.000
## Max. :9.000#splite train and test dataset
wine_train <- winedata[1:3750, ]
wine_test <- winedata[3751:4898, ]
#Model on the Data on train dataset
m.rpart <- rpart(quality ~ ., data=wine_train)
summary(m.rpart)## Call:
## rpart(formula = quality ~ ., data = wine_train)
## n= 3750
##
## CP nsplit rel error xerror xstd
## 1 0.15501053 0 1.0000000 1.0002630 0.02445079
## 2 0.05098911 1 0.8449895 0.8477242 0.02335875
## 3 0.02796998 2 0.7940004 0.8062166 0.02281638
## 4 0.01970128 3 0.7660304 0.7803097 0.02159532
## 5 0.01265926 4 0.7463291 0.7661249 0.02092459
## 6 0.01007193 5 0.7336698 0.7582832 0.02077944
## 7 0.01000000 6 0.7235979 0.7486815 0.02050843
##
## Variable importance
## alcohol density volatile.acidity
## 34 21 15
## chlorides total.sulfur.dioxide free.sulfur.dioxide
## 11 7 6
## residual.sugar sulphates citric.acid
## 3 1 1
##
## Node number 1: 3750 observations, complexity param=0.1550105
## mean=5.870933, MSE=0.7854751
## left son=2 (2372 obs) right son=3 (1378 obs)
## Primary splits:
## alcohol < 10.85 to the left, improve=0.15501050, (0 missing)
## density < 0.992035 to the right, improve=0.10915940, (0 missing)
## chlorides < 0.0395 to the right, improve=0.07682258, (0 missing)
## total.sulfur.dioxide < 158.5 to the right, improve=0.04089663, (0 missing)
## citric.acid < 0.235 to the left, improve=0.03636458, (0 missing)
## Surrogate splits:
## density < 0.991995 to the right, agree=0.869, adj=0.644, (0 split)
## chlorides < 0.0375 to the right, agree=0.757, adj=0.339, (0 split)
## total.sulfur.dioxide < 103.5 to the right, agree=0.690, adj=0.155, (0 split)
## residual.sugar < 5.375 to the right, agree=0.667, adj=0.094, (0 split)
## sulphates < 0.345 to the right, agree=0.647, adj=0.038, (0 split)
##
## Node number 2: 2372 observations, complexity param=0.05098911
## mean=5.604975, MSE=0.5981709
## left son=4 (1611 obs) right son=5 (761 obs)
## Primary splits:
## volatile.acidity < 0.2275 to the right, improve=0.10585250, (0 missing)
## free.sulfur.dioxide < 13.5 to the left, improve=0.03390500, (0 missing)
## citric.acid < 0.235 to the left, improve=0.03204075, (0 missing)
## alcohol < 10.11667 to the left, improve=0.03136524, (0 missing)
## chlorides < 0.0585 to the right, improve=0.01633599, (0 missing)
## Surrogate splits:
## pH < 3.485 to the left, agree=0.694, adj=0.047, (0 split)
## sulphates < 0.755 to the left, agree=0.685, adj=0.020, (0 split)
## total.sulfur.dioxide < 105.5 to the right, agree=0.683, adj=0.011, (0 split)
## residual.sugar < 0.75 to the right, agree=0.681, adj=0.007, (0 split)
## chlorides < 0.0285 to the right, agree=0.680, adj=0.003, (0 split)
##
## Node number 3: 1378 observations, complexity param=0.02796998
## mean=6.328737, MSE=0.7765472
## left son=6 (84 obs) right son=7 (1294 obs)
## Primary splits:
## free.sulfur.dioxide < 10.5 to the left, improve=0.07699080, (0 missing)
## alcohol < 11.76667 to the left, improve=0.06210660, (0 missing)
## total.sulfur.dioxide < 67.5 to the left, improve=0.04438619, (0 missing)
## residual.sugar < 1.375 to the left, improve=0.02905351, (0 missing)
## fixed.acidity < 7.35 to the right, improve=0.02613259, (0 missing)
## Surrogate splits:
## total.sulfur.dioxide < 53.5 to the left, agree=0.952, adj=0.214, (0 split)
## volatile.acidity < 0.875 to the right, agree=0.940, adj=0.024, (0 split)
##
## Node number 4: 1611 observations, complexity param=0.01265926
## mean=5.43203, MSE=0.5098121
## left son=8 (688 obs) right son=9 (923 obs)
## Primary splits:
## volatile.acidity < 0.3025 to the right, improve=0.04540111, (0 missing)
## alcohol < 10.05 to the left, improve=0.03874403, (0 missing)
## free.sulfur.dioxide < 13.5 to the left, improve=0.03338886, (0 missing)
## chlorides < 0.0495 to the right, improve=0.02574623, (0 missing)
## citric.acid < 0.195 to the left, improve=0.02327981, (0 missing)
## Surrogate splits:
## citric.acid < 0.215 to the left, agree=0.633, adj=0.141, (0 split)
## free.sulfur.dioxide < 20.5 to the left, agree=0.600, adj=0.063, (0 split)
## chlorides < 0.0595 to the right, agree=0.593, adj=0.047, (0 split)
## residual.sugar < 1.15 to the left, agree=0.583, adj=0.023, (0 split)
## total.sulfur.dioxide < 219.25 to the right, agree=0.582, adj=0.022, (0 split)
##
## Node number 5: 761 observations
## mean=5.971091, MSE=0.5878633
##
## Node number 6: 84 observations
## mean=5.369048, MSE=1.137613
##
## Node number 7: 1294 observations, complexity param=0.01970128
## mean=6.391036, MSE=0.6894405
## left son=14 (629 obs) right son=15 (665 obs)
## Primary splits:
## alcohol < 11.76667 to the left, improve=0.06504696, (0 missing)
## chlorides < 0.0395 to the right, improve=0.02758705, (0 missing)
## fixed.acidity < 7.35 to the right, improve=0.02750932, (0 missing)
## pH < 3.055 to the left, improve=0.02307356, (0 missing)
## total.sulfur.dioxide < 191.5 to the right, improve=0.02186818, (0 missing)
## Surrogate splits:
## density < 0.990885 to the right, agree=0.720, adj=0.424, (0 split)
## volatile.acidity < 0.2675 to the left, agree=0.637, adj=0.253, (0 split)
## chlorides < 0.0365 to the right, agree=0.630, adj=0.238, (0 split)
## residual.sugar < 1.475 to the left, agree=0.575, adj=0.126, (0 split)
## total.sulfur.dioxide < 128.5 to the right, agree=0.574, adj=0.124, (0 split)
##
## Node number 8: 688 observations
## mean=5.255814, MSE=0.4054895
##
## Node number 9: 923 observations
## mean=5.56338, MSE=0.5471747
##
## Node number 14: 629 observations, complexity param=0.01007193
## mean=6.173291, MSE=0.6838017
## left son=28 (11 obs) right son=29 (618 obs)
## Primary splits:
## volatile.acidity < 0.465 to the right, improve=0.06897561, (0 missing)
## total.sulfur.dioxide < 200 to the right, improve=0.04223066, (0 missing)
## residual.sugar < 0.975 to the left, improve=0.03061714, (0 missing)
## fixed.acidity < 7.35 to the right, improve=0.02978501, (0 missing)
## sulphates < 0.575 to the left, improve=0.02165970, (0 missing)
## Surrogate splits:
## citric.acid < 0.045 to the left, agree=0.986, adj=0.182, (0 split)
## total.sulfur.dioxide < 279.25 to the right, agree=0.986, adj=0.182, (0 split)
##
## Node number 15: 665 observations
## mean=6.596992, MSE=0.6075098
##
## Node number 28: 11 observations
## mean=4.545455, MSE=0.9752066
##
## Node number 29: 618 observations
## mean=6.202265, MSE=0.6306098#Model Visualization
rpart.plot(m.rpart, digits=3, type=1)
#Model evaluation
p.rpart <- predict(m.rpart, wine_test)
summary(p.rpart)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.545 5.563 5.971 5.893 6.202 6.597summary(wine_test$quality) ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.901 6.000 9.000Q3- What is your interpretation about this amount of RMSE?
if (!require("Metrics")) {
install.packages("Metrics")
library(Metrics)
}## Loading required package: Metrics## Warning: package 'Metrics' was built under R version 3.6.3rmse(wine_test$quality, p.rpart)## [1] 0.7448093#the lower RMSE value can be used to see if the model fit the observed data well.The lower the better.
#The root-mean-square error is 0.745.It is still high.Method #4. News Popularity
#Import the data
Popularity<- read.csv(file="F:/homework/530/OnlineNewsPopularity_for_R.csv", na.strings='?')
# View(news)
str(Popularity)## 'data.frame': 39644 obs. of 61 variables:
## $ url : Factor w/ 39644 levels "http://mashable.com/2013/01/07/amazon-instant-video-browser/",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ timedelta : num 731 731 731 731 731 731 731 731 731 731 ...
## $ n_tokens_title : num 12 9 9 9 13 10 8 12 11 10 ...
## $ n_tokens_content : num 219 255 211 531 1072 ...
## $ n_unique_tokens : num 0.664 0.605 0.575 0.504 0.416 ...
## $ n_non_stop_words : num 1 1 1 1 1 ...
## $ n_non_stop_unique_tokens : num 0.815 0.792 0.664 0.666 0.541 ...
## $ num_hrefs : num 4 3 3 9 19 2 21 20 2 4 ...
## $ num_self_hrefs : num 2 1 1 0 19 2 20 20 0 1 ...
## $ num_imgs : num 1 1 1 1 20 0 20 20 0 1 ...
## $ num_videos : num 0 0 0 0 0 0 0 0 0 1 ...
## $ average_token_length : num 4.68 4.91 4.39 4.4 4.68 ...
## $ num_keywords : num 5 4 6 7 7 9 10 9 7 5 ...
## $ data_channel_is_lifestyle : num 0 0 0 0 0 0 1 0 0 0 ...
## $ data_channel_is_entertainment: num 1 0 0 1 0 0 0 0 0 0 ...
## $ data_channel_is_bus : num 0 1 1 0 0 0 0 0 0 0 ...
## $ data_channel_is_socmed : num 0 0 0 0 0 0 0 0 0 0 ...
## $ data_channel_is_tech : num 0 0 0 0 1 1 0 1 1 0 ...
## $ data_channel_is_world : num 0 0 0 0 0 0 0 0 0 1 ...
## $ kw_min_min : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_max_min : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_avg_min : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_min_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_max_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_avg_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_min_avg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_max_avg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_avg_avg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ self_reference_min_shares : num 496 0 918 0 545 8500 545 545 0 0 ...
## $ self_reference_max_shares : num 496 0 918 0 16000 8500 16000 16000 0 0 ...
## $ self_reference_avg_sharess : num 496 0 918 0 3151 ...
## $ weekday_is_monday : num 1 1 1 1 1 1 1 1 1 1 ...
## $ weekday_is_tuesday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_wednesday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_thursday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_friday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_saturday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_sunday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ is_weekend : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LDA_00 : num 0.5003 0.7998 0.2178 0.0286 0.0286 ...
## $ LDA_01 : num 0.3783 0.05 0.0333 0.4193 0.0288 ...
## $ LDA_02 : num 0.04 0.0501 0.0334 0.4947 0.0286 ...
## $ LDA_03 : num 0.0413 0.0501 0.0333 0.0289 0.0286 ...
## $ LDA_04 : num 0.0401 0.05 0.6822 0.0286 0.8854 ...
## $ global_subjectivity : num 0.522 0.341 0.702 0.43 0.514 ...
## $ global_sentiment_polarity : num 0.0926 0.1489 0.3233 0.1007 0.281 ...
## $ global_rate_positive_words : num 0.0457 0.0431 0.0569 0.0414 0.0746 ...
## $ global_rate_negative_words : num 0.0137 0.01569 0.00948 0.02072 0.01213 ...
## $ rate_positive_words : num 0.769 0.733 0.857 0.667 0.86 ...
## $ rate_negative_words : num 0.231 0.267 0.143 0.333 0.14 ...
## $ avg_positive_polarity : num 0.379 0.287 0.496 0.386 0.411 ...
## $ min_positive_polarity : num 0.1 0.0333 0.1 0.1364 0.0333 ...
## $ max_positive_polarity : num 0.7 0.7 1 0.8 1 0.6 1 1 0.8 0.5 ...
## $ avg_negative_polarity : num -0.35 -0.119 -0.467 -0.37 -0.22 ...
## $ min_negative_polarity : num -0.6 -0.125 -0.8 -0.6 -0.5 -0.4 -0.5 -0.5 -0.125 -0.5 ...
## $ max_negative_polarity : num -0.2 -0.1 -0.133 -0.167 -0.05 ...
## $ title_subjectivity : num 0.5 0 0 0 0.455 ...
## $ title_sentiment_polarity : num -0.188 0 0 0 0.136 ...
## $ abs_title_subjectivity : num 0 0.5 0.5 0.5 0.0455 ...
## $ abs_title_sentiment_polarity : num 0.188 0 0 0 0.136 ...
## $ shares : int 593 711 1500 1200 505 855 556 891 3600 710 ...#minify instances
PopularitysShort <- data.frame(Popularity$n_tokens_title, Popularity$n_tokens_content, Popularity$n_unique_tokens, Popularity$n_non_stop_words, Popularity$num_hrefs, Popularity$num_imgs, Popularity$num_videos, Popularity$average_token_length, Popularity$num_keywords, Popularity$kw_max_max, Popularity$global_sentiment_polarity, Popularity$avg_positive_polarity, Popularity$title_subjectivity, Popularity$title_sentiment_polarity, Popularity$abs_title_subjectivity, Popularity$abs_title_sentiment_polarity, Popularity$shares)
colnames(PopularitysShort) <- c("n_tokens_title", "n_tokens_content", "n_unique_tokens", "n_non_stop_words", "num_hrefs", "num_imgs", "num_videos", "average_token_length", "num_keywords", "kw_max_max", "global_sentiment_polarity", "avg_positive_polarity", "title_subjectivity", "title_sentiment_polarity", "abs_title_subjectivity", "abs_title_sentiment_polarity", "shares")
str(PopularitysShort)## 'data.frame': 39644 obs. of 17 variables:
## $ n_tokens_title : num 12 9 9 9 13 10 8 12 11 10 ...
## $ n_tokens_content : num 219 255 211 531 1072 ...
## $ n_unique_tokens : num 0.664 0.605 0.575 0.504 0.416 ...
## $ n_non_stop_words : num 1 1 1 1 1 ...
## $ num_hrefs : num 4 3 3 9 19 2 21 20 2 4 ...
## $ num_imgs : num 1 1 1 1 20 0 20 20 0 1 ...
## $ num_videos : num 0 0 0 0 0 0 0 0 0 1 ...
## $ average_token_length : num 4.68 4.91 4.39 4.4 4.68 ...
## $ num_keywords : num 5 4 6 7 7 9 10 9 7 5 ...
## $ kw_max_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ global_sentiment_polarity : num 0.0926 0.1489 0.3233 0.1007 0.281 ...
## $ avg_positive_polarity : num 0.379 0.287 0.496 0.386 0.411 ...
## $ title_subjectivity : num 0.5 0 0 0 0.455 ...
## $ title_sentiment_polarity : num -0.188 0 0 0 0.136 ...
## $ abs_title_subjectivity : num 0 0.5 0.5 0.5 0.0455 ...
## $ abs_title_sentiment_polarity: num 0.188 0 0 0 0.136 ...
## $ shares : int 593 711 1500 1200 505 855 556 891 3600 710 ...Processing data
PopularitysShort$popular = rep('na', nrow(PopularitysShort))
for(i in 1:39644) {
if(PopularitysShort$shares[i] >= 1400) {
PopularitysShort$popular[i] = "1"}
else {PopularitysShort$popular[i] = "0"}
}
PopularitysShort$shares = PopularitysShort$popular
PopularitysShort$shares <- as.factor(PopularitysShort$shares)
set.seed(12345)
Popularitys_rand <- PopularitysShort[order(runif(10000)), ]
#Split the data into training and test datasets
Popularitys_train <- Popularitys_rand[1:9000, ]
Popularitys_test <- Popularitys_rand[9001:10000, ]
prop.table(table(Popularitys_train$shares))##
## 0 1
## 0.4308889 0.5691111prop.table(table(Popularitys_test$shares))##
## 0 1
## 0.414 0.586Model training
library("C50", lib.loc="~/R/win-library/3.5")
Popularitys_model <- C5.0(Popularitys_train[-17], Popularitys_train$shares)
summary(Popularitys_model)##
## Call:
## C5.0.default(x = Popularitys_train[-17], y = Popularitys_train$shares)
##
##
## C5.0 [Release 2.07 GPL Edition] Fri Jun 19 18:29:22 2020
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 9000 cases (18 attributes) from undefined.data
##
## Decision tree:
##
## popular = 0: 0 (3878)
## popular = 1: 1 (5122)
##
##
## Evaluation on training data (9000 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 2 0( 0.0%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 3878 (a): class 0
## 5122 (b): class 1
##
##
## Attribute usage:
##
## 100.00% popular
##
##
## Time: 0.1 secsEvaluate the model
#Evaluate the model
Popularitys_pred <- predict(Popularitys_model, Popularitys_test)
(p <- table(Popularitys_pred, Popularitys_test$shares))##
## Popularitys_pred 0 1
## 0 414 0
## 1 0 586(Accuracy <- sum(diag(p))/sum(p)*100)## [1] 100Q4- Try decision tree and random forest and evaluate the model.
Popularity<- read.csv(file="F:/homework/530/OnlineNewsPopularity_for_R.csv", na.strings='?')
Popularity <- Popularity[,-(1:2)]
#Check for outliers
Popularity=Popularity[!Popularity$n_unique_tokens==701,]
#minify instances
PopularityShort <- data.frame(Popularity$n_tokens_title, Popularity$n_tokens_content, Popularity$n_unique_tokens, Popularity$n_non_stop_words, Popularity$num_hrefs, Popularity$num_imgs, Popularity$num_videos, Popularity$average_token_length, Popularity$num_keywords, Popularity$kw_max_max, Popularity$global_sentiment_polarity, Popularity$avg_positive_polarity, Popularity$title_subjectivity, Popularity$title_sentiment_polarity, Popularity$abs_title_subjectivity, Popularity$abs_title_sentiment_polarity, Popularity$shares)
colnames(PopularityShort) <- c("n_tokens_title", "n_tokens_content", "n_unique_tokens", "n_non_stop_words", "num_hrefs", "num_imgs", "num_videos", "average_token_length", "num_keywords", "kw_max_max", "global_sentiment_polarity", "avg_positive_polarity", "title_subjectivity", "title_sentiment_polarity", "abs_title_subjectivity", "abs_title_sentiment_polarity", "shares")
#Standardize the dataset
for(i in ncol(Popularity)-1){
Popularity[,i]<-scale(Popularity[,i], center = TRUE, scale = TRUE)
}
#Define popular articles
PopularityShort$shares <- as.factor(ifelse(PopularityShort$shares > 1400,1,0))
set.seed(12346)
Popularity_rand <- PopularityShort[order(runif(39643)), ]
Popularity_train <- Popularity_rand[1:3964, ]
Popularity_test <- Popularity_rand[3965:39643, ]
Popularity_train$shares <- as.factor(Popularity_train$shares)
random_modelNews <- randomForest(Popularity_train$shares ~ . , data= Popularity_train)
#Model training
cred_pridRF <- predict(random_modelNews, Popularity_test)
(p2 <- table(cred_pridRF, Popularity_test$shares))##
## cred_pridRF 0 1
## 0 10014 7264
## 1 8088 10313#Accuracy
(Accuracy <- sum(diag(p2))/sum(p2)*100)## [1] 56.97189#importance
importance(random_modelNews)## MeanDecreaseGini
## n_tokens_title 110.95451
## n_tokens_content 172.15879
## n_unique_tokens 188.13602
## n_non_stop_words 168.50493
## num_hrefs 148.12312
## num_imgs 90.89631
## num_videos 52.40012
## average_token_length 195.31143
## num_keywords 93.64084
## kw_max_max 49.10667
## global_sentiment_polarity 214.07528
## avg_positive_polarity 187.35883
## title_subjectivity 74.85371
## title_sentiment_polarity 82.44230
## abs_title_subjectivity 73.17472
## abs_title_sentiment_polarity 68.02149Step 2: Decision Tree
#Evaluate the model
Popularity_model <- C5.0(Popularity_train[-17], Popularity_train$shares)
summary(Popularity_model)##
## Call:
## C5.0.default(x = Popularity_train[-17], y = Popularity_train$shares)
##
##
## C5.0 [Release 2.07 GPL Edition] Fri Jun 19 18:29:34 2020
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 3964 cases (17 attributes) from undefined.data
##
## Decision tree:
##
## global_sentiment_polarity > 0.1079453:
## :...num_hrefs > 12:
## : :...n_tokens_title <= 10: 1 (436/135)
## : : n_tokens_title > 10:
## : : :...abs_title_subjectivity > 0.3416667: 1 (137/47)
## : : abs_title_subjectivity <= 0.3416667:
## : : :...n_unique_tokens <= 0.3832077: 1 (7)
## : : n_unique_tokens > 0.3832077: 0 (123/51)
## : num_hrefs <= 12:
## : :...abs_title_subjectivity > 0.1272727: 1 (1135/509)
## : abs_title_subjectivity <= 0.1272727:
## : :...num_imgs <= 1:
## : :...n_unique_tokens > 0.4742152: 0 (255/87)
## : : n_unique_tokens <= 0.4742152:
## : : :...n_tokens_title <= 12: 1 (22/3)
## : : n_tokens_title > 12: 0 (5)
## : num_imgs > 1:
## : :...num_imgs <= 6: 1 (47/16)
## : num_imgs > 6:
## : :...kw_max_max <= 227300: 1 (6/1)
## : kw_max_max > 227300: 0 (44/15)
## global_sentiment_polarity <= 0.1079453:
## :...num_hrefs > 19: 1 (186/79)
## num_hrefs <= 19:
## :...n_non_stop_words <= 0.9999999: 1 (112/51)
## n_non_stop_words > 0.9999999:
## :...num_keywords <= 6:
## :...num_hrefs <= 1: 0 (39/4)
## : num_hrefs > 1:
## : :...title_sentiment_polarity <= 0.1083333: 0 (449/140)
## : title_sentiment_polarity > 0.1083333:
## : :...num_keywords <= 5: 1 (74/28)
## : num_keywords > 5:
## : :...average_token_length <= 4.411043: 1 (9/1)
## : average_token_length > 4.411043: 0 (48/14)
## num_keywords > 6:
## :...num_imgs > 2: 1 (226/102)
## num_imgs <= 2:
## :...num_imgs > 0:
## :...kw_max_max <= 227300: 1 (25/10)
## : kw_max_max > 227300: 0 (437/159)
## num_imgs <= 0:
## :...num_keywords <= 7: 1 (46/15)
## num_keywords > 7:
## :...num_videos > 15: 1 (5)
## num_videos <= 15:
## :...num_hrefs <= 2: 1 (10/2)
## num_hrefs > 2: 0 (81/30)
##
##
## Evaluation on training data (3964 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 25 1499(37.8%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 981 999 (a): class 0
## 500 1484 (b): class 1
##
##
## Attribute usage:
##
## 100.00% num_hrefs
## 100.00% global_sentiment_polarity
## 44.93% abs_title_subjectivity
## 39.38% n_non_stop_words
## 36.55% num_keywords
## 30.50% num_imgs
## 18.42% n_tokens_title
## 14.63% title_sentiment_polarity
## 12.92% kw_max_max
## 10.39% n_unique_tokens
## 2.42% num_videos
## 1.44% average_token_length
##
##
## Time: 0.1 secs#Model training
Popularity_pred <- predict(Popularity_model, Popularity_test)
CrossTable(Popularity_test$shares, Popularity_pred, prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE, dnn = c('actual shares', 'predicted shares'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35679
##
##
## | predicted shares
## actual shares | 0 | 1 | Row Total |
## --------------|-----------|-----------|-----------|
## 0 | 7692 | 10410 | 18102 |
## | 0.216 | 0.292 | |
## --------------|-----------|-----------|-----------|
## 1 | 5596 | 11981 | 17577 |
## | 0.157 | 0.336 | |
## --------------|-----------|-----------|-----------|
## Column Total | 13288 | 22391 | 35679 |
## --------------|-----------|-----------|-----------|
##
## (p3 <- table(Popularity_pred, Popularity_test$shares))##
## Popularity_pred 0 1
## 0 7692 5596
## 1 10410 11981#Accuracy
(Accuracy <- sum(diag(p3))/sum(p3)*100)## [1] 55.13888