Car Evaluation Analysis

2024-04-03

topic

18k 词

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
2
3

car_eval <- read.csv("car.data.txt", header=FALSE)
colnames(car_eval)<-c("buying","maint","doors","persons","lug_boot","safety","class")
head(car_eval)

##   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 unacc

Exploratory 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_evalpred_log_reg<-apply(probability,1,which.max)
car_evalpred_log_reg[which(car_evalpred_log_reg=="1")]<-levels(car_evalclass)[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_evalpred_log_reg=="3")]<-levels(car_evalclass)[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      0.98161

2 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_evalpred_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      0.76863

Non-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      0.87362

4 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_evalpred_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      0.99158

6 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      1.00000

7 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      0.76863

8 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      0.99699

9 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      0.81358

10 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      0.79940

Non-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      0.89639

12 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      0.92157

13 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      0.99850

14 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      1.00000

15 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       0.699


  
    

  
    
    
      
        Lua作图教程之设计模式
        
    
  
  

    
      
        Lua元表 · 花生肉泥

最新文章

归档

汽车数据R语言机器学习分析

Load the data 读取数据

Exploratory Data Analysis 探索数据

'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

summary(model1)

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 0.98161

2 Linear Discriminant Analysis 线性判别

summary(model2)

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 0.76863

Non-Linear Classification 非线性分类

3 Mixture Discriminant Analysis 混合判别

summary(model3)

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

`$ class : Factor w/ 4 levels "acc","good","unacc",…: 3 3 3 3 3 3 3 3 3 3 …`

`Balanced Accuracy 0.9226 0.92482 0.9502 0.98161`

`Balanced Accuracy 0.9176 0.66215 0.9577 0.76863`

`Balanced Accuracy 0.9146 0.86263 0.9559 0.87362`

`Balanced Accuracy 0.8629 0.94175 0.9430 0.99158`