如何在 R 中为 xgboost 创建混淆矩阵
how to create a confusion matrix for xgboost in R
我已经在 R 中创建了我的 XGBoost 分类器，如下面的代码
#importing the dataset
XGBoostDataSet_Hr_Admin_8 <- read.csv("CompletedDataImputed_HR_Admin.csv")
#Use factor function to convert categorical data to numerical data
XGBoostDataSet_Hr_Admin_8$Salary = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Salary, levels =c('L','M', 'H', 'V'), labels =c(1,2,3,4)))
XGBoostDataSet_Hr_Admin_8$Rude_Behavior = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Rude_Behavior, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Feeling_undervalued =as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Feeling_undervalued, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Overall_satisfaction = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Overall_satisfaction, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Raises_frozen = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Raises_frozen, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Poor_Conditions = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Poor_Conditions, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Growth_not_available = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Growth_not_available, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Workplace_Conflict = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Workplace_Conflict, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Employee_Turnover, levels=c('Y', 'N'), labels =c(1,0)))
#split the data in train dataset and test dataset
library(caTools)
split = sample.split(XGBoostDataSet_Hr_Admin_8$Employee_Turnover,SplitRatio = 0.7)
training_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==TRUE)
test_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==FALSE)
#fitting XGBoost to the Training Test
library(xgboost)
classifier9 = xgboost(data = as.matrix(training_set8[-10]), label = training_set8$Employee_Turnover, nrounds = 10)
现在，我需要为 XGBoost 创建一个混淆矩阵。
我在网上搜索过，很遗憾没有找到解决方案。
谁能帮帮我。
提前致谢
您可以使用 caret::confusionMatrix() 函数，但您需要对输出进行一些处理。显然你需要一个真实结果的向量（测试数据集），来比较计算结果和真实结果：
library(xgboost)
#Use factor function to convert categorical data to numerical data
XGBoostDataSet_Hr_Admin_8$Salary = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Salary, levels =c('L','M', 'H', 'V'), labels =c(1,2,3,4)))
XGBoostDataSet_Hr_Admin_8$Rude_Behavior = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Rude_Behavior, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Feeling_undervalued =as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Feeling_undervalued, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Overall_satisfaction = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Overall_satisfaction, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Raises_frozen = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Raises_frozen, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Poor_Conditions = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Poor_Conditions, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Growth_not_available = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Growth_not_available, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Workplace_Conflict = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Workplace_Conflict, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Employee_Turnover, levels=c('Y', 'N'), labels =c(1,0)))
# here ifelse 0 1
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = ifelse(XGBoostDataSet_Hr_Admin_8$Employee_Turnover == 1,0,1)
library(caTools)
split = sample.split(XGBoostDataSet_Hr_Admin_8$Employee_Turnover,SplitRatio = 0.7)
training_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==TRUE)
test_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==FALSE)
bst <- xgboost(data = as.matrix(training_set8[,-10]), label = training_set8$Employee_Turnover, max_depth = 2,
eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
# you've to do your prediction here
pred <- predict(bst, as.matrix(test_set8[,-10]))
# and transform them in a 0 1 variable, you can choose the value to get 1
pred <- as.numeric(pred > 0.5)
library(caret)
confusionMatrix(factor(pred),factor(test_set8$Employee_Turnover))
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 67 2
1 0 16
Accuracy : 0.9765
95% CI : (0.9176, 0.9971)
No Information Rate : 0.7882
P-Value [Acc > NIR] : 4.626e-07
Kappa : 0.9265
Mcnemar's Test P-Value : 0.4795
Sensitivity : 1.0000
Specificity : 0.8889
Pos Pred Value : 0.9710
Neg Pred Value : 1.0000
Prevalence : 0.7882
Detection Rate : 0.7882
Detection Prevalence : 0.8118
Balanced Accuracy : 0.9444
'Positive' Class : 0
以后提问的时候提供一些示例数据。
下面的代码使用 predict.xgb.Booster
中的示例创建了一个混淆矩阵
library("xgboost")
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
## Predict class probability for new data
pred <- predict(bst, test$data)
## Use arbitrary cutoff of 0.5 for classifier
table(test$label, as.numeric(pred > 0.5))
# 0 1
# 0 825 10
# 1 9 767
注意事项，您需要将您的training_set8$Employee_Turnover转换为0和1。希望你已经做到了，如果没有看到我下面的例子。
其次，需要说明，objective = "binary:logistic"做xgboost的时候，这个做分类。
所以从你拥有的开始：
library(caTools)
library(xgboost)
library(caret)
set.seed(12345)
# reproducible results
XGBoostDataSet_Hr_Admin_8 <- read.csv("CompletedDataImputed_HR_Admin.csv")
#Use factor function to convert categorical data to numerical data
XGBoostDataSet_Hr_Admin_8$Salary = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Salary, levels =c('L','M', 'H', 'V'), labels =c(1,2,3,4)))
XGBoostDataSet_Hr_Admin_8$Rude_Behavior = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Rude_Behavior, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Feeling_undervalued =as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Feeling_undervalued, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Overall_satisfaction = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Overall_satisfaction, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Raises_frozen = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Raises_frozen, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Poor_Conditions = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Poor_Conditions, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Growth_not_available = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Growth_not_available, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Workplace_Conflict = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Workplace_Conflict, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
对于这部分，我们将标签正确设置为 0 和 1
#set levels
lvl = c('N', 'Y')
# sorry I have to do it like this, it's too long for me to read
lb = as.character(XGBoostDataSet_Hr_Admin_8$Employee_Turnover)
lb = as.numeric(factor(lb,levels=lvl))-1
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = lb
我们按照您的方式拆分为训练 + 测试：
#split the data in train dataset and test dataset
split = sample.split(XGBoostDataSet_Hr_Admin_8$Employee_Turnover,SplitRatio = 0.7)
training_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==TRUE)
test_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==FALSE)
合身：
#fitting XGBoost to the Training Test
classifier9 = xgboost(data = as.matrix(training_set8[-10]),
label = training_set8$Employee_Turnover, nrounds = 10)
现在我们根据概率得到预测并转换
pred <- predict(classifier9, as.matrix(training_set8[-10]))
# we convert to predicted labels
pred_label <- lvl[as.numeric(pred>0.5)+1]
# we get the observed label, or iris$Species
actual_label <- lvl[as.numeric(training_set8$Employee_Turnover)+1]
最后一个混淆矩阵：
# confusion matrix
table(pred_label,actual_label)
actual_label
pred_label N Y
N 41 0
Y 0 158
或使用插入符号：
confusionMatrix(factor(pred_label,levels=lvl),
factor(actual_label,levels=lvl))
Confusion Matrix and Statistics
Reference
Prediction N Y
N 41 0
Y 0 158
这是实际数据（OP友情提供）：
structure(list(Salary = structure(c(2L, 3L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L,
3L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
3L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 3L, 2L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 2L, 3L), .Label = c("H", "L", "M", "V"), class = "factor"),
Percentage_Increment = c(5, 10, 7, 7, 5, 7, 5, 5, 10, 5,
5, 5, 5, 5, 5, 10, 5, 5, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5,
5, 10, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 7,
5, 5, 10, 7, 5, 5, 5, 5, 10, 10, 10, 5, 5, 5, 7, 10, 5, 5,
5, 7, 10, 5, 7, 5, 5, 10, 10, 10, 5, 5, 10, 5, 5, 5, 5, 5,
5, 5, 5, 10, 5, 5, 7, 7, 5, 10, 5, 5, 5, 5, 5, 7, 5, 10,
5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5,
5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 10, 5, 5,
5, 7, 5, 7, 10, 7, 10, 5, 10, 10, 5, 7, 5, 5, 10, 5, 5, 5,
10, 5, 7, 5, 5, 5, 5, 10, 3, 5, 5, 10, 10, 5, 5, 7, 10, 5,
5, 5, 5, 5, 5, 5, 10, 5, 7, 5, 5, 5, 5, 5, 7, 5, 7, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 7, 5, 5, 5, 10, 10, 5, 5, 5,
10, 5, 10, 10, 10, 10, 7, 5, 7, 5, 5, 10, 1, 10, 30, 1, 0.02,
5, 1, 11, 1, 3, 10, 1, 11, 1, 5, 10, 2.2, 18, 4, 10, 8, 1,
5, 9, 5, 4, 15, 15, 4, 10, 12, 1, 9, 3, 2.5, 5, 20, 30, 10,
5, 100, 10, 1, 1, 8, 1, 1, 2, 1, 5, 10, 1, 50, 50, 2, 3,
25, 1, 1), Rude_Behavior = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 3L,
3L, 1L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 1L), .Label = c("M", "N",
"Y"), class = "factor"), Feeling_undervalued = structure(c(1L,
2L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L,
3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 1L,
3L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 2L, 3L, 3L, 1L,
2L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 3L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L), .Label = c("M",
"N", "Y"), class = "factor"), Overall_satisfaction = structure(c(2L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 2L,
1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 1L,
2L, 3L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 1L), .Label = c("M",
"N", "Y"), class = "factor"), Poor_Conditions = structure(c(3L,
1L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L,
3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 1L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 3L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 3L,
3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L), .Label = c("M",
"N", "Y"), class = "factor"), Raises_frozen = structure(c(2L,
3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L,
2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L,
2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 1L, 3L,
1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 2L, 1L,
3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L), .Label = c("M",
"N", "Y"), class = "factor"), Growth_not_available = structure(c(1L,
3L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 2L, 3L,
2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 1L,
3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L,
1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 1L,
3L, 3L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 3L,
3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 2L,
2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 3L, 1L, 3L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L,
3L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L), .Label = c("M",
"N", "Y"), class = "factor"), Workplace_Conflict = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 3L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L,
2L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 1L,
3L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L,
1L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 3L, 2L), .Label = c("M",
"N", "Y"), class = "factor"), Employee_Turnover = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("N",
"Y"), class = "factor")), class = "data.frame", row.names = c(NA,
-284L))
我已经在 R 中创建了我的 XGBoost 分类器，如下面的代码
#importing the dataset
XGBoostDataSet_Hr_Admin_8 <- read.csv("CompletedDataImputed_HR_Admin.csv")
#Use factor function to convert categorical data to numerical data
XGBoostDataSet_Hr_Admin_8$Salary = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Salary, levels =c('L','M', 'H', 'V'), labels =c(1,2,3,4)))
XGBoostDataSet_Hr_Admin_8$Rude_Behavior = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Rude_Behavior, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Feeling_undervalued =as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Feeling_undervalued, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Overall_satisfaction = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Overall_satisfaction, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Raises_frozen = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Raises_frozen, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Poor_Conditions = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Poor_Conditions, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Growth_not_available = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Growth_not_available, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Workplace_Conflict = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Workplace_Conflict, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Employee_Turnover, levels=c('Y', 'N'), labels =c(1,0)))
#split the data in train dataset and test dataset
library(caTools)
split = sample.split(XGBoostDataSet_Hr_Admin_8$Employee_Turnover,SplitRatio = 0.7)
training_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==TRUE)
test_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==FALSE)
#fitting XGBoost to the Training Test
library(xgboost)
classifier9 = xgboost(data = as.matrix(training_set8[-10]), label = training_set8$Employee_Turnover, nrounds = 10)
现在，我需要为 XGBoost 创建一个混淆矩阵。
我在网上搜索过，很遗憾没有找到解决方案。
谁能帮帮我。
提前致谢
您可以使用 caret::confusionMatrix() 函数，但您需要对输出进行一些处理。显然你需要一个真实结果的向量（测试数据集），来比较计算结果和真实结果：
library(xgboost)
#Use factor function to convert categorical data to numerical data
XGBoostDataSet_Hr_Admin_8$Salary = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Salary, levels =c('L','M', 'H', 'V'), labels =c(1,2,3,4)))
XGBoostDataSet_Hr_Admin_8$Rude_Behavior = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Rude_Behavior, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Feeling_undervalued =as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Feeling_undervalued, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Overall_satisfaction = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Overall_satisfaction, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Raises_frozen = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Raises_frozen, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Poor_Conditions = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Poor_Conditions, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Growth_not_available = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Growth_not_available, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Workplace_Conflict = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Workplace_Conflict, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Employee_Turnover, levels=c('Y', 'N'), labels =c(1,0)))
# here ifelse 0 1
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = ifelse(XGBoostDataSet_Hr_Admin_8$Employee_Turnover == 1,0,1)
library(caTools)
split = sample.split(XGBoostDataSet_Hr_Admin_8$Employee_Turnover,SplitRatio = 0.7)
training_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==TRUE)
test_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==FALSE)
bst <- xgboost(data = as.matrix(training_set8[,-10]), label = training_set8$Employee_Turnover, max_depth = 2,
eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
# you've to do your prediction here
pred <- predict(bst, as.matrix(test_set8[,-10]))
# and transform them in a 0 1 variable, you can choose the value to get 1
pred <- as.numeric(pred > 0.5)
library(caret)
confusionMatrix(factor(pred),factor(test_set8$Employee_Turnover))
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 67 2
1 0 16
Accuracy : 0.9765
95% CI : (0.9176, 0.9971)
No Information Rate : 0.7882
P-Value [Acc > NIR] : 4.626e-07
Kappa : 0.9265
Mcnemar's Test P-Value : 0.4795
Sensitivity : 1.0000
Specificity : 0.8889
Pos Pred Value : 0.9710
Neg Pred Value : 1.0000
Prevalence : 0.7882
Detection Rate : 0.7882
Detection Prevalence : 0.8118
Balanced Accuracy : 0.9444
'Positive' Class : 0
以后提问的时候提供一些示例数据。
下面的代码使用 predict.xgb.Booster
library("xgboost")
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic")
## Predict class probability for new data
pred <- predict(bst, test$data)
## Use arbitrary cutoff of 0.5 for classifier
table(test$label, as.numeric(pred > 0.5))
# 0 1
# 0 825 10
# 1 9 767
注意事项，您需要将您的training_set8$Employee_Turnover转换为0和1。希望你已经做到了，如果没有看到我下面的例子。
其次，需要说明，objective = "binary:logistic"做xgboost的时候，这个做分类。
所以从你拥有的开始：
library(caTools)
library(xgboost)
library(caret)
set.seed(12345)
# reproducible results
XGBoostDataSet_Hr_Admin_8 <- read.csv("CompletedDataImputed_HR_Admin.csv")
#Use factor function to convert categorical data to numerical data
XGBoostDataSet_Hr_Admin_8$Salary = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Salary, levels =c('L','M', 'H', 'V'), labels =c(1,2,3,4)))
XGBoostDataSet_Hr_Admin_8$Rude_Behavior = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Rude_Behavior, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Feeling_undervalued =as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Feeling_undervalued, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Overall_satisfaction = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Overall_satisfaction, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Raises_frozen = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Raises_frozen, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Poor_Conditions = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Poor_Conditions, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Growth_not_available = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Growth_not_available, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
XGBoostDataSet_Hr_Admin_8$Workplace_Conflict = as.numeric(factor(XGBoostDataSet_Hr_Admin_8$Workplace_Conflict, levels=c('Y', 'M', 'N'), labels =c(1,2,3)))
对于这部分，我们将标签正确设置为 0 和 1
#set levels
lvl = c('N', 'Y')
# sorry I have to do it like this, it's too long for me to read
lb = as.character(XGBoostDataSet_Hr_Admin_8$Employee_Turnover)
lb = as.numeric(factor(lb,levels=lvl))-1
XGBoostDataSet_Hr_Admin_8$Employee_Turnover = lb
我们按照您的方式拆分为训练 + 测试：
#split the data in train dataset and test dataset
split = sample.split(XGBoostDataSet_Hr_Admin_8$Employee_Turnover,SplitRatio = 0.7)
training_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==TRUE)
test_set8 = subset(XGBoostDataSet_Hr_Admin_8, split==FALSE)
合身：
#fitting XGBoost to the Training Test
classifier9 = xgboost(data = as.matrix(training_set8[-10]),
label = training_set8$Employee_Turnover, nrounds = 10)
现在我们根据概率得到预测并转换
pred <- predict(classifier9, as.matrix(training_set8[-10]))
# we convert to predicted labels
pred_label <- lvl[as.numeric(pred>0.5)+1]
# we get the observed label, or iris$Species
actual_label <- lvl[as.numeric(training_set8$Employee_Turnover)+1]
最后一个混淆矩阵：
# confusion matrix
table(pred_label,actual_label)
actual_label
pred_label N Y
N 41 0
Y 0 158
或使用插入符号：
confusionMatrix(factor(pred_label,levels=lvl),
factor(actual_label,levels=lvl))
Confusion Matrix and Statistics
Reference
Prediction N Y
N 41 0
Y 0 158
这是实际数据（OP友情提供）：
structure(list(Salary = structure(c(2L, 3L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L,
3L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
3L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 3L, 2L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 2L, 3L), .Label = c("H", "L", "M", "V"), class = "factor"),
Percentage_Increment = c(5, 10, 7, 7, 5, 7, 5, 5, 10, 5,
5, 5, 5, 5, 5, 10, 5, 5, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5,
5, 10, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 7,
5, 5, 10, 7, 5, 5, 5, 5, 10, 10, 10, 5, 5, 5, 7, 10, 5, 5,
5, 7, 10, 5, 7, 5, 5, 10, 10, 10, 5, 5, 10, 5, 5, 5, 5, 5,
5, 5, 5, 10, 5, 5, 7, 7, 5, 10, 5, 5, 5, 5, 5, 7, 5, 10,
5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5,
5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 10, 5, 5,
5, 7, 5, 7, 10, 7, 10, 5, 10, 10, 5, 7, 5, 5, 10, 5, 5, 5,
10, 5, 7, 5, 5, 5, 5, 10, 3, 5, 5, 10, 10, 5, 5, 7, 10, 5,
5, 5, 5, 5, 5, 5, 10, 5, 7, 5, 5, 5, 5, 5, 7, 5, 7, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 7, 5, 5, 5, 10, 10, 5, 5, 5,
10, 5, 10, 10, 10, 10, 7, 5, 7, 5, 5, 10, 1, 10, 30, 1, 0.02,
5, 1, 11, 1, 3, 10, 1, 11, 1, 5, 10, 2.2, 18, 4, 10, 8, 1,
5, 9, 5, 4, 15, 15, 4, 10, 12, 1, 9, 3, 2.5, 5, 20, 30, 10,
5, 100, 10, 1, 1, 8, 1, 1, 2, 1, 5, 10, 1, 50, 50, 2, 3,
25, 1, 1), Rude_Behavior = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 3L,
3L, 1L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 1L), .Label = c("M", "N",
"Y"), class = "factor"), Feeling_undervalued = structure(c(1L,
2L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L,
3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 1L,
3L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 2L, 3L, 3L, 1L,
2L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 3L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L), .Label = c("M",
"N", "Y"), class = "factor"), Overall_satisfaction = structure(c(2L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 2L,
3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 2L,
1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 1L,
2L, 3L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 1L), .Label = c("M",
"N", "Y"), class = "factor"), Poor_Conditions = structure(c(3L,
1L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L,
3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L,
3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L,
3L, 1L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 3L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 3L,
3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L), .Label = c("M",
"N", "Y"), class = "factor"), Raises_frozen = structure(c(2L,
3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L,
2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L,
2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L,
2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 1L, 3L,
1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 2L, 1L,
3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L), .Label = c("M",
"N", "Y"), class = "factor"), Growth_not_available = structure(c(1L,
3L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L,
2L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 2L, 3L,
2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 1L,
3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L,
1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 1L,
3L, 3L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 3L,
3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 2L,
2L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 3L, 1L, 3L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L,
3L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L), .Label = c("M",
"N", "Y"), class = "factor"), Workplace_Conflict = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 3L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 3L, 3L,
2L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 2L, 3L, 3L,
2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 1L,
3L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L,
1L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 3L, 2L), .Label = c("M",
"N", "Y"), class = "factor"), Employee_Turnover = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("N",
"Y"), class = "factor")), class = "data.frame", row.names = c(NA,
-284L))