汽车数据R语言机器学习分析
- title: “Car Evaluation Analysis”
- author: “Suraj Vidyadaran”
- date: “Sunday, February 21, 2016”
- output: md_document
对汽车数据使用17种分类算法进行数据分析,对代码进行实践应用和内容翻译
- Load the data 读取数据
- Exploratory Data Analysis 探索数据
- Classification Analysis 分类
Load the data 读取数据
buying 价格 maint 大小 doors 门 persons 载人 lug_boot 轮子 safety 安全 class 等级
1 | car_eval <- read.csv("car.data.txt", header=FALSE) |
## buying maint doors persons lug_boot safety class
## 1 vhigh vhigh 2 2 small low unacc
## 2 vhigh vhigh 2 2 small med unacc
## 3 vhigh vhigh 2 2 small high unacc
## 4 vhigh vhigh 2 2 med low unacc
## 5 vhigh vhigh 2 2 med med unacc
## 6 vhigh vhigh 2 2 med high unaccExploratory Data Analysis 探索数据
# summary(car_eval)
str(car_eval)
## 'data.frame': 1728 obs. of 7 variables:
## $ buying : Factor w/ 4 levels "high","low","med",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ maint : Factor w/ 4 levels "high","low","med",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ doors : Factor w/ 4 levels "2","3","4","5more": 1 1 1 1 1 1 1 1 1 1 ...
## $ persons : Factor w/ 3 levels "2","4","more": 1 1 1 1 1 1 1 1 1 2 ...
## $ lug_boot: Factor w/ 3 levels "big","med","small": 3 3 3 2 2 2 1 1 1 3 ...
## $ safety : Factor w/ 3 levels "high","low","med": 2 3 1 2 3 1 2 3 1 2 ...
## $ class : Factor w/ 4 levels "acc","good","unacc",..: 3 3 3 3 3 3 3 3 3 3 ...Classification Analysis 分类
Linear Classification 线性分类
1 Logistic Regression 逻辑回归
library(VGAM) # 广义线性模型
#Build the model
model1<-vglm(class~buying+maint+doors+persons+lug_boot+safety,family = "multinomial",data=car_eval)
#Summarize the model
# summary(model1)
#Predict using the model
x<-car_eval[,1:6]
y<-car_eval[,7]
probability<-predict(model1,x,type="response") # 相应就是二元
car_eval$pred_log_reg<-apply(probability,1,which.max)
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="1")]<-levels(car_eval$class)[1]
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="2")]<-levels(car_eval$class)[2]
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="3")]<-levels(car_eval$class)[3]
car_eval$pred_log_reg[which(car_eval$pred_log_reg=="4")]<-levels(car_eval$class)[4]
#Accuracy of the model
mtab<-table(car_eval$pred_log_reg,car_eval$class)
library(caret)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 340 5 47 2
## good 8 59 1 0
## unacc 31 0 1162 0
## vgood 5 5 0 63
##
## Overall Statistics
##
## Accuracy : 0.9398
## 95% CI : (0.9275, 0.9506)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8699
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.8854 0.85507 0.9603 0.96923
## Specificity 0.9598 0.99458 0.9402 0.99399
## Pos Pred Value 0.8629 0.86765 0.9740 0.86301
## Neg Pred Value 0.9670 0.99398 0.9103 0.99879
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.1968 0.03414 0.6725 0.03646
## Detection Prevalence 0.2280 0.03935 0.6904 0.04225
## Balanced Accuracy 0.9226 0.92482 0.9502 0.981612 Linear Discriminant Analysis 线性判别
library(caret)
library(MASS)
#Build the model
model2<-lda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model2)
#Predict using the model
car_eval$pred_lda<-predict(model2,x)$class
#Accuracy of the model
mtab<-table(car_eval$pred_lda,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 361 44 70 27
## good 10 23 2 3
## unacc 13 0 1138 0
## vgood 0 2 0 35
##
## Overall Statistics
##
## Accuracy : 0.901
## 95% CI : (0.886, 0.9147)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7883
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9401 0.33333 0.9405 0.53846
## Specificity 0.8951 0.99096 0.9749 0.99880
## Pos Pred Value 0.7191 0.60526 0.9887 0.94595
## Neg Pred Value 0.9812 0.97278 0.8752 0.98226
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2089 0.01331 0.6586 0.02025
## Detection Prevalence 0.2905 0.02199 0.6661 0.02141
## Balanced Accuracy 0.9176 0.66215 0.9577 0.76863Non-Linear Classification 非线性分类
3 Mixture Discriminant Analysis 混合判别
library(mda)
#Build the model
model3<-mda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model3)
#Predict using the model
car_eval$pred_mda<-predict(model3,x)
#Accuracy of the model
mtab<-table(car_eval$pred_mda,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 353 13 92 16
## good 20 51 3 0
## unacc 5 0 1115 0
## vgood 6 5 0 49
##
## Overall Statistics
##
## Accuracy : 0.9074
## 95% CI : (0.8928, 0.9207)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.808
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9193 0.73913 0.9215 0.75385
## Specificity 0.9100 0.98614 0.9903 0.99339
## Pos Pred Value 0.7447 0.68919 0.9955 0.81667
## Neg Pred Value 0.9753 0.98912 0.8438 0.99041
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2043 0.02951 0.6453 0.02836
## Detection Prevalence 0.2743 0.04282 0.6481 0.03472
## Balanced Accuracy 0.9146 0.86263 0.9559 0.873624 Quadratic Discriminant Analysis 二次判别
library(MASS)
#Build the model
model4<-qda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model4)
#Predict using the model
car_eval$pred_qda<-predict(model4,x)
#Accuracy of the model
mtab<-table(car_eval$pred_qda,car_eval$class)
confusionMatrix(mtab)5 Regularized Discriminant Analysis 正则约束判别
library(klaR)
#Build the model
model5<-rda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,gamma = 0.05,lambda = 0.01)
#Summarize the model
# summary(model5)
#Predict using the model
car_eval$pred_rda<-predict(model5,x)$class
#Accuracy of the model
mtab<-table(car_eval$pred_rda,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 317 0 134 0
## good 45 63 4 0
## unacc 0 0 1072 0
## vgood 22 6 0 65
##
## Overall Statistics
##
## Accuracy : 0.8779
## 95% CI : (0.8615, 0.893)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7572
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.8255 0.91304 0.8860 1.00000
## Specificity 0.9003 0.97046 1.0000 0.98316
## Pos Pred Value 0.7029 0.56250 1.0000 0.69892
## Neg Pred Value 0.9475 0.99629 0.7896 1.00000
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.1834 0.03646 0.6204 0.03762
## Detection Prevalence 0.2610 0.06481 0.6204 0.05382
## Balanced Accuracy 0.8629 0.94175 0.9430 0.991586 Neural Network 神经网络
library(nnet)
#Build the model
model6<-nnet(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,size = 4,decay = 0.0001,maxit = 500)
## # weights: 84
## initial value 4315.884207
## iter 10 value 494.299222
## iter 20 value 271.980488
## iter 30 value 201.767107
## iter 40 value 170.953147
## iter 50 value 110.980954
## iter 60 value 56.771482
## iter 70 value 41.572251
## iter 80 value 34.479180
## iter 90 value 30.761660
## iter 100 value 29.156225
## iter 110 value 28.361403
## iter 120 value 27.944054
## iter 130 value 27.553794
## iter 140 value 26.698026
## iter 150 value 25.411406
## iter 160 value 24.996274
## iter 170 value 24.851429
## iter 180 value 24.830112
## iter 190 value 24.735811
## iter 200 value 24.426379
## iter 210 value 23.802677
## iter 220 value 23.478896
## iter 230 value 23.307991
## iter 240 value 23.164112
## iter 250 value 23.047281
## iter 260 value 22.863117
## iter 270 value 22.802107
## iter 280 value 22.739661
## iter 290 value 22.689016
## iter 300 value 22.625042
## iter 310 value 22.595026
## iter 320 value 22.581459
## iter 330 value 22.573767
## iter 340 value 22.566224
## iter 350 value 22.565535
## iter 360 value 22.564483
## iter 370 value 22.560267
## iter 380 value 22.557433
## iter 390 value 22.555073
## iter 400 value 22.552698
## iter 410 value 22.550126
## iter 420 value 22.548059
## iter 430 value 22.546974
## iter 440 value 22.546031
## iter 450 value 22.545549
## iter 460 value 22.545069
## iter 470 value 22.544765
## iter 480 value 22.544515
## iter 490 value 22.544258
## iter 500 value 22.544140
## final value 22.544140
## stopped after 500 iterations
#Summarize the model
# summary(model6)
#Predict using the model
car_eval$pred_nnet<-predict(model6,x,type="class")
#Accuracy of the model
mtab<-table(car_eval$pred_nnet,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 384 0 5 0
## good 0 69 0 0
## unacc 0 0 1205 0
## vgood 0 0 0 65
##
## Overall Statistics
##
## Accuracy : 0.9971
## 95% CI : (0.9933, 0.9991)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9937
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 1.0000 1.00000 0.9959 1.00000
## Specificity 0.9963 1.00000 1.0000 1.00000
## Pos Pred Value 0.9871 1.00000 1.0000 1.00000
## Neg Pred Value 1.0000 1.00000 0.9904 1.00000
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2222 0.03993 0.6973 0.03762
## Detection Prevalence 0.2251 0.03993 0.6973 0.03762
## Balanced Accuracy 0.9981 1.00000 0.9979 1.000007 Flexible Discriminant Analysis 弹性判别
library(mda)
#Build the model
model7<-fda(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model7)
#Predict using the model
car_eval$pred_fda<-predict(model7,x,type="class")
#Accuracy of the model
mtab<-table(car_eval$pred_fda,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 361 44 72 27
## good 10 23 2 3
## unacc 13 0 1136 0
## vgood 0 2 0 35
##
## Overall Statistics
##
## Accuracy : 0.8999
## 95% CI : (0.8848, 0.9136)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.786
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9401 0.33333 0.9388 0.53846
## Specificity 0.8936 0.99096 0.9749 0.99880
## Pos Pred Value 0.7163 0.60526 0.9887 0.94595
## Neg Pred Value 0.9812 0.97278 0.8722 0.98226
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2089 0.01331 0.6574 0.02025
## Detection Prevalence 0.2917 0.02199 0.6649 0.02141
## Balanced Accuracy 0.9169 0.66215 0.9569 0.768638 Support Vector Machine 支持向量机
library(kernlab)
#Build the model
model8<-ksvm(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model8)
#Predict using the model
car_eval$pred_svm<-predict(model8,x,type="response")
#Accuracy of the model
mtab<-table(car_eval$pred_svm,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 375 0 33 0
## good 8 60 3 0
## unacc 0 0 1174 0
## vgood 1 9 0 65
##
## Overall Statistics
##
## Accuracy : 0.9688
## 95% CI : (0.9594, 0.9764)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9333
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9766 0.86957 0.9702 1.00000
## Specificity 0.9754 0.99337 1.0000 0.99399
## Pos Pred Value 0.9191 0.84507 1.0000 0.86667
## Neg Pred Value 0.9932 0.99457 0.9350 1.00000
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2170 0.03472 0.6794 0.03762
## Detection Prevalence 0.2361 0.04109 0.6794 0.04340
## Balanced Accuracy 0.9760 0.93147 0.9851 0.996999 k-Nearest Neighbors k近邻
library(caret)
#Build the model
model9<-knn3(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,k=5)
#Summarize the model
# summary(model9)
#Predict using the model
car_eval$pred_knn<-predict(model9,x,type="class")
#Accuracy of the model
mtab<-table(car_eval$pred_knn,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 332 47 11 19
## good 6 13 0 4
## unacc 44 5 1199 1
## vgood 2 4 0 41
##
## Overall Statistics
##
## Accuracy : 0.9172
## 95% CI : (0.9032, 0.9298)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8118
## Mcnemar's Test P-Value : 2.234e-13
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.8646 0.188406 0.9909 0.63077
## Specificity 0.9427 0.993972 0.9035 0.99639
## Pos Pred Value 0.8117 0.565217 0.9600 0.87234
## Neg Pred Value 0.9606 0.967155 0.9770 0.98572
## Prevalence 0.2222 0.039931 0.7002 0.03762
## Detection Rate 0.1921 0.007523 0.6939 0.02373
## Detection Prevalence 0.2367 0.013310 0.7228 0.02720
## Balanced Accuracy 0.9036 0.591189 0.9472 0.8135810 Naive Bayes 朴素贝叶斯
library(e1071)
#Build the model
model10<-naiveBayes(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,k=5)
#Summarize the model
# summary(model10)
#Predict using the model
car_eval$pred_naive<-predict(model10,x)
#Accuracy of the model
mtab<-table(car_eval$pred_naive,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 289 46 47 26
## good 10 21 2 0
## unacc 85 0 1161 0
## vgood 0 2 0 39
##
## Overall Statistics
##
## Accuracy : 0.8738
## 95% CI : (0.8573, 0.8891)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7139
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.7526 0.30435 0.9595 0.60000
## Specificity 0.9115 0.99277 0.8359 0.99880
## Pos Pred Value 0.7083 0.63636 0.9318 0.95122
## Neg Pred Value 0.9280 0.97168 0.8983 0.98459
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.1672 0.01215 0.6719 0.02257
## Detection Prevalence 0.2361 0.01910 0.7211 0.02373
## Balanced Accuracy 0.8320 0.64856 0.8977 0.79940Non-Linear Classification with Decision Trees 非线性树模型
11 Classification and Regression Trees(CART) 分类回归树
library(rpart)
#Build the model
model11<-rpart(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model11)
#Predict using the model
car_eval$pred_cart<-predict(model11,x,type="class")
#Accuracy of the model
mtab<-table(car_eval$pred_cart,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 358 0 45 13
## good 16 60 4 0
## unacc 7 0 1161 0
## vgood 3 9 0 52
##
## Overall Statistics
##
## Accuracy : 0.9439
## 95% CI : (0.9319, 0.9542)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8806
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9323 0.86957 0.9595 0.80000
## Specificity 0.9568 0.98794 0.9865 0.99278
## Pos Pred Value 0.8606 0.75000 0.9940 0.81250
## Neg Pred Value 0.9802 0.99454 0.9125 0.99219
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2072 0.03472 0.6719 0.03009
## Detection Prevalence 0.2407 0.04630 0.6759 0.03704
## Balanced Accuracy 0.9446 0.92875 0.9730 0.8963912 C4.5 C4.5树
library(caret)
library(RWeka)
#Build the model
model12<-J48(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model12)
#Predict using the model
car_eval$pred_c45<-predict(model12,x)
#Accuracy of the model
mtab<-table(car_eval$pred_c45,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 380 9 35 4
## good 2 57 3 6
## unacc 0 0 1172 0
## vgood 2 3 0 55
##
## Overall Statistics
##
## Accuracy : 0.963
## 95% CI : (0.9529, 0.9714)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9207
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9896 0.82609 0.9686 0.84615
## Specificity 0.9643 0.99337 1.0000 0.99699
## Pos Pred Value 0.8879 0.83824 1.0000 0.91667
## Neg Pred Value 0.9969 0.99277 0.9317 0.99400
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2199 0.03299 0.6782 0.03183
## Detection Prevalence 0.2477 0.03935 0.6782 0.03472
## Balanced Accuracy 0.9769 0.90973 0.9843 0.9215713 PART 分区树
library(RWeka)
#Build the model
model13<-PART(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model13)
#Predict using the model
car_eval$pred_part<-predict(model13,x)
#Accuracy of the model
mtab<-table(car_eval$pred_part,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 374 3 13 0
## good 1 63 1 0
## unacc 6 1 1196 0
## vgood 3 2 0 65
##
## Overall Statistics
##
## Accuracy : 0.9826
## 95% CI : (0.9753, 0.9883)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9622
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9740 0.91304 0.9884 1.00000
## Specificity 0.9881 0.99879 0.9865 0.99699
## Pos Pred Value 0.9590 0.96923 0.9942 0.92857
## Neg Pred Value 0.9925 0.99639 0.9733 1.00000
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2164 0.03646 0.6921 0.03762
## Detection Prevalence 0.2257 0.03762 0.6962 0.04051
## Balanced Accuracy 0.9810 0.95592 0.9875 0.9985014 Bagging CART 背包树
library(ipred)
#Build the model
model14<-bagging(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model14)
#Predict using the model
car_eval$pred_bagging<-predict(model14,x)
#Accuracy of the model
mtab<-table(car_eval$pred_bagging,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 384 0 0 0
## good 0 69 0 0
## unacc 0 0 1210 0
## vgood 0 0 0 65
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9979, 1)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 1.0000 1.00000 1.0000 1.00000
## Specificity 1.0000 1.00000 1.0000 1.00000
## Pos Pred Value 1.0000 1.00000 1.0000 1.00000
## Neg Pred Value 1.0000 1.00000 1.0000 1.00000
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2222 0.03993 0.7002 0.03762
## Detection Prevalence 0.2222 0.03993 0.7002 0.03762
## Balanced Accuracy 1.0000 1.00000 1.0000 1.0000015 Random Forest 随机森林
library(randomForest)
#Build the model
model15<-randomForest(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval)
#Summarize the model
# summary(model15)
#Predict using the model
car_eval$pred_randomforest<-predict(model15,x)
#Accuracy of the model
mtab<-table(car_eval$pred_randomforest,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 384 0 1 0
## good 0 69 0 0
## unacc 0 0 1209 0
## vgood 0 0 0 65
##
## Overall Statistics
##
## Accuracy : 0.9994
## 95% CI : (0.9968, 1)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9987
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 1.0000 1.00000 0.9992 1.00000
## Specificity 0.9993 1.00000 1.0000 1.00000
## Pos Pred Value 0.9974 1.00000 1.0000 1.00000
## Neg Pred Value 1.0000 1.00000 0.9981 1.00000
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2222 0.03993 0.6997 0.03762
## Detection Prevalence 0.2228 0.03993 0.6997 0.03762
## Balanced Accuracy 0.9996 1.00000 0.9996 1.0000016 Gradient Boosted Machine 梯度加速机器
library(gbm)
#Build the model
model16<-gbm(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,distribution="multinomial")
#Summarize the model
# summary(model16)
#Predict using the model
probability<-predict(model16,x,n.trees=1)
car_eval$pred_gbm<-colnames(probability)[apply(probability,1,which.max)]
#Accuracy of the model
mtab<-table(car_eval$pred_gbm,car_eval$class)
mtab
##
## acc good unacc vgood
## unacc 384 69 1210 6517 Boosted C5.0 加速树
library(C50)
#Build the model
model17<-C5.0(class~buying+maint+doors+persons+lug_boot+safety,data=car_eval,trials=10)
#Summarize the model
# summary(model17)
#Predict using the model
car_eval$pred_c50<-predict(model17,x)
#Accuracy of the model
mtab<-table(car_eval$pred_c50,car_eval$class)
confusionMatrix(mtab)
## Confusion Matrix and Statistics
##
##
## acc good unacc vgood
## acc 383 0 3 2
## good 0 69 0 0
## unacc 1 0 1207 0
## vgood 0 0 0 63
##
## Overall Statistics
##
## Accuracy : 0.9965
## 95% CI : (0.9925, 0.9987)
## No Information Rate : 0.7002
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9924
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: acc Class: good Class: unacc Class: vgood
## Sensitivity 0.9974 1.00000 0.9975 0.96923
## Specificity 0.9963 1.00000 0.9981 1.00000
## Pos Pred Value 0.9871 1.00000 0.9992 1.00000
## Neg Pred Value 0.9993 1.00000 0.9942 0.99880
## Prevalence 0.2222 0.03993 0.7002 0.03762
## Detection Rate 0.2216 0.03993 0.6985 0.03646
## Detection Prevalence 0.2245 0.03993 0.6991 0.03646
## Balanced Accuracy 0.9968 1.00000 0.9978 0.98462- THE END