前言

```#载入分析所需要的包
library(dplyr)
library(devtools)
library(woe)
library(ROSE)
library(rpart)
library(rpart.plot)
library(ggplot2)
require(caret)
library(pROC)```

```dat_nba<-read.csv('nba_2017_nba_players_with_salary.csv')
dat_nba\$cut_salary<-ifelse(dat_nba\$SALARY_MILLIONS>15,1,0)
dat_nba\$cut_salary<-as.factor(dat_nba\$cut_salary)
dat_nba<-select(dat_nba,-PLAYER,-SALARY_MILLIONS,-TEAM)
cat('目标变量：\n')
summary(dat_nba\$cut_salary)
cat('\n')
names(dat_nba)```

0 1

291 51

[1] “X” “Rk” “POSITION” “AGE” “MP” “FG”

[7] “FGA” “FG.” “X3P” “X3PA” “X3P.” “X2P”

[13] “X2PA” “X2P.” “eFG.” “FT” “FTA” “FT.”

[19] “ORB” “DRB” “TRB” “AST” “STL” “BLK”

[25] “TOV” “PF” “POINTS” “GP” “MPG” “ORPM”

[31] “DRPM” “RPM” “WINS_RPM” “PIE” “PACE” “W”

```#install_github("riv","tomasgreif")
#library(devtools)
#library(woe)
IV<-iv.mult(dat_nba,"cut_salary",TRUE)   #原理是以Y作为被解释变量，其他作为解释变量，建立决策树模型
iv.plot.summary(IV)```

```#install.packages("ROSE")
#library(ROSE)
# 过采样&下采样
datt1<-dat_nba
table(datt1\$cut_salary)
data_balanced_both <- ovun.sample(cut_salary ~ ., data = datt1, method = "both", p=0.5,N=342,seed = 1)\$data
table(data_balanced_both\$cut_salary)```

0 1

291 51

0 1

183 159

```#library(rpart)

#设置随机分配，查分数据为train集和test集#
dat=data_balanced_both
smp_size <- floor(0.6 * nrow(dat))
set.seed(123)
train_ind <- sample(seq_len(nrow(dat)), size = smp_size)
train <- dat[train_ind, ]
test <- dat[-train_ind, ]
dim(train)
dim(test)

fit<-(cut_salary~.)
rtree<-rpart(fit,minsplit=10, cp=0.03,data=train)
printcp(rtree)

#library(rpart.plot) #调出rpart.plot包
rpart.plot(rtree, type=2)```

Warning message:

In strsplit(code, “\n”, fixed = TRUE) :

input string 1 is invalid in this locale

[1] 205 37

[1] 137 37

Classification tree:

rpart(formula = fit, data = train, minsplit = 10, cp = 0.03)

Variables actually used in tree construction:

[1] FT GP PF TRB WINS_RPM

Root node error: 93/205 = 0.45366

n= 205

CP nsplit rel error xerror xstd

1 0.548387 0 1.00000 1.00000 0.076646

2 0.118280 1 0.45161 0.50538 0.064717

3 0.043011 2 0.33333 0.40860 0.059826

4 0.032258 3 0.29032 0.34409 0.055878

5 0.030000 5 0.22581 0.33333 0.055156

```#检验预测效果#
pre_train<-predict(rtree,type = 'vector') #type = c("vector", "prob", "class", "matrix"),
table(pre_train,train\$cut_salary)

#检验test集预测效果#
pre_test<-predict(rtree, newdata = test,type = 'vector')
table(pre_test, test\$cut_salary)

#检验整体集预测效果#
pre_dat<-predict(rtree, newdata = datt1,type = 'class')
table(pre_dat, datt1\$cut_salary)```

train集： 0 1

99 8

13 85

test集 0 1

60 13

11 53

pre_dat 0 1

237 10

54 41

```result=datt1
result\$true_label=result\$MobDr1to6_od15
result\$pre_prob=pre_dat
#install.packages("gmodels")
TPR <- NULL
FPR <- NULL
for(i in seq(from=1,to=0,by=-0.1)){
#判为正类实际也为正类
TP <- sum((result\$pre_prob >= i) * (result\$true_label == 1))
#判为正类实际为负类
FP <- sum((result\$pre_prob >= i) * (result\$true_label == 0))
#判为负类实际为负类
TN <- sum((result\$pre_prob < i) * (result\$true_label == 0))
#判为负类实际为正类
FN <- sum((result\$pre_prob < i) * (result\$true_label == 1))
TPR <- c(TPR,TP/(TP+FN))
FPR <- c(FPR,FP/(FP+TN))
}

max(TPR-FPR)  #KS

#library(ggplot2)
ggplot(data=NULL,mapping = aes(x=seq(0,1,0.1),y=TPR))+
geom_point()+
geom_smooth(se=FALSE,formula = y ~ splines::ns(x,10), method ='lm')+
geom_line(mapping = aes(x=seq(0,1,0.1),y=FPR),linetype=6)```

```KS值为：
[1] 0.3277339```

```# 找到KS值对应的切分点：
for (i in seq(0,10,1)){
print(i)
print(TPR[i]-FPR[i])
}
## 混肴矩阵
result\$pre_to1<-ifelse(result\$pre_prob>=0.7,1,0)
#require(caret)
xtab<-table(result\$pre_to1,result\$true_label)
confusionMatrix(xtab)```

[1] 0

numeric(0)

[1] 1

[1] 0

[1] 2

[1] 0

[1] 3

[1] 0.6066303

[1] 4

[1] 0.6183546

[1] 5

[1] 0.6183546

[1] 6

[1] 0.6183546

[1] 7

[1] 0.6183546

[1] 8

[1] 0.6183546

[1] 9

[1] 0.6183546

[1] 10

[1] 0.6183546

Confusion Matrix and Statistics

0 1

0 237 10

1 54 41

Accuracy : 0.8129

95% CI : (0.7674, 0.8528)

No Information Rate : 0.8509

P-Value [Acc > NIR] : 0.9772

Kappa : 0.4561

Mcnemar’s Test P-Value : 7.658e-08

Sensitivity : 0.8144

Specificity : 0.8039

Pos Pred Value : 0.9595

Neg Pred Value : 0.4316

Prevalence : 0.8509

Detection Rate : 0.6930

Detection Prevalence : 0.7222

Balanced Accuracy : 0.8092

‘Positive’ Class : 0

```## roc曲线及AUC
#library(pROC)
datt1_pro<-predict(rtree, newdata = datt1,type = 'prob')
datt1\$pre_prob<-datt1_pro[,2]
modelroc <- roc(datt1\$cut_salary,datt1\$pre_prob)
plot(modelroc, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),
grid.col=c("green", "red"), max.auc.polygon=TRUE,
auc.polygon.col="skyblue", print.thres=TRUE)```

```#设置随机分配，查分数据为train集和test集#
dat=datt1
smp_size <- floor(0.5 * nrow(dat))
train_ind <- sample(seq_len(nrow(dat)), size = smp_size)
train_2 <- dat[train_ind, ]
test_2 <- dat[-train_ind, ]
dim(train_2)
dim(test_2)

#检验预测效果#
pre_train_2<-predict(rtree,newdata=train_2,type = 'vector')
table(pre_train_2,train_2\$cut_salary)

#检验test集预测效果#
pre_test_2<-predict(rtree, newdata = test_2,type = 'vector')

table(pre_test_2, test_2\$cut_salary)

pre_train_2p<-predict(rtree,newdata=train_2,type = 'prob')
train_2\$pre<-pre_train_2p[,2]
modelroc <- roc(train_2\$cut_salary,train_2\$pre)
plot(modelroc, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),
grid.col=c("green", "red"), max.auc.polygon=TRUE,
auc.polygon.col="skyblue", print.thres=TRUE)

pre_test_2p<-predict(rtree, newdata = test_2,type = 'prob')
test_2\$pre<-pre_test_2p[,2]
modelroc <- roc(test_2\$cut_salary,test_2\$pre)
plot(modelroc, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2),
grid.col=c("green", "red"), max.auc.polygon=TRUE,
auc.polygon.col="skyblue", print.thres=TRUE)```

[1] 171 38

[1] 171 38

pre_train_2 0 1

1 114 2

2 31 24

pre_test_2 0 1

1 123 8

2 23 17