我尝试在这里处理一些数据并比较 glm 和 lda 的测试性能。
数据附在此处。
这是我尝试实现这两点的总体计划:
training = read.csv("train.csv")
testing = read.csv("test.csv")
model_glm <- glm(V1 ~.,family=binomial(link='logit'),data=training)
pred_glm <- predict(model_glm, testing)
library(MASS)
model_lda <- lda(V1 ~ ., data=training)
predict_lda <- predict(model_lda, testing)
#Calculating classification error
err_lda <- (pred_lda) - test$V1
err2_lda <- err_lda[err_lda != 0]
classification_error_lda = length(err2_lda)/length(test$V1)
但是这些都不起作用。我以为有一个多项式家庭类，但这似乎不存在。另外，由于我的第一列是数字，下一列都是灰度值，所以我认为我这样做了 V1 ~ .，但我认为这对于这些情况也不正确。有人知道我的语法/设置是否错误吗？
编辑:我添加了如何尝试计算 LDA 的分类误差。然而，我认为我原来的东西不起作用，因为它给出了:
Error in (pred_lda) - test$V1 : non-numeric argument to binary operator
最佳答案
这不是二元分类，而是多类(数字)分类问题，其中我们有 10 个类标签。因此，您需要使用多项式 Logit，而不是逻辑回归。尝试以下操作，我们可以看到，多项 Logit 模型的预测总体精度高于 lda。
library(nnet)
model_mlogit <- multinom(V1 ~ ., data = training, MaxNWts=2581)
predict_mlogit <- predict(model_mlogit, testing)
library(MASS)
model_lda <- lda(V1 ~ ., data=training)
predict_lda <- predict(model_lda, testing)
library(caret)
confusionMatrix(predict_mlogit,testing$V1)
# output
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3 4 5 6 7 8 9
0 343 0 5 2 5 4 1 0 7 0
1 0 254 1 0 2 1 0 0 0 0
2 3 2 163 4 5 0 4 2 7 0
3 2 1 6 145 1 7 0 3 3 1
4 3 1 8 1 168 3 4 5 1 3
5 2 0 1 8 2 137 4 0 9 1
6 2 1 1 1 4 3 156 0 0 0
7 3 1 5 2 1 0 0 132 4 2
8 1 1 7 3 4 2 1 0 130 5
9 0 3 1 0 8 3 0 5 5 165
Overall Statistics
Accuracy : 0.8934
95% CI : (0.879, 0.9065)
No Information Rate : 0.1789
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.8803
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8 Class: 9
Sensitivity 0.9554 0.9621 0.82323 0.87349 0.84000 0.85625 0.91765 0.89796 0.78313 0.93220
Specificity 0.9854 0.9977 0.98507 0.98696 0.98395 0.98538 0.99347 0.99032 0.98696 0.98634
Pos Pred Value 0.9346 0.9845 0.85789 0.85799 0.85279 0.83537 0.92857 0.88000 0.84416 0.86842
Neg Pred Value 0.9902 0.9943 0.98074 0.98857 0.98232 0.98752 0.99239 0.99192 0.98057 0.99340
Prevalence 0.1789 0.1315 0.09865 0.08271 0.09965 0.07972 0.08470 0.07324 0.08271 0.08819
Detection Rate 0.1709 0.1266 0.08122 0.07225 0.08371 0.06826 0.07773 0.06577 0.06477 0.08221
Detection Prevalence 0.1829 0.1286 0.09467 0.08421 0.09816 0.08171 0.08371 0.07474 0.07673 0.09467
Balanced Accuracy 0.9704 0.9799 0.90415 0.93023 0.91198 0.92082 0.95556 0.94414 0.88505 0.95927
confusionMatrix(predict_lda$class,testing$V1)
#output
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3 4 5 6 7 8 9
0 342 0 7 3 1 6 1 0 5 0
1 0 251 2 0 4 0 0 1 0 0
2 0 0 157 3 6 0 3 0 2 0
3 4 2 4 142 0 16 0 2 11 0
4 3 5 12 3 174 3 3 7 7 4
5 1 0 2 9 0 125 3 0 4 0
6 5 3 1 0 2 0 157 0 0 0
7 0 0 1 1 2 0 0 129 0 5
8 3 1 12 4 1 5 3 1 135 3
9 1 2 0 1 10 5 0 7 2 165
Overall Statistics
Accuracy : 0.8854
95% CI : (0.8706, 0.899)
No Information Rate : 0.1789
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.8713
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8 Class: 9
Sensitivity 0.9526 0.9508 0.79293 0.85542 0.87000 0.78125 0.92353 0.87755 0.81325 0.93220
Specificity 0.9860 0.9960 0.99226 0.97882 0.97399 0.98971 0.99401 0.99516 0.98207 0.98470
Pos Pred Value 0.9370 0.9729 0.91813 0.78453 0.78733 0.86806 0.93452 0.93478 0.80357 0.85492
Neg Pred Value 0.9896 0.9926 0.97767 0.98686 0.98544 0.98121 0.99293 0.99037 0.98314 0.99338
Prevalence 0.1789 0.1315 0.09865 0.08271 0.09965 0.07972 0.08470 0.07324 0.08271 0.08819
Detection Rate 0.1704 0.1251 0.07823 0.07075 0.08670 0.06228 0.07823 0.06428 0.06726 0.08221
Detection Prevalence 0.1819 0.1286 0.08520 0.09018 0.11011 0.07175 0.08371 0.06876 0.08371 0.09616
Balanced Accuracy 0.9693 0.9734 0.89260 0.91712 0.92200 0.88548 0.95877 0.93636 0.89766 0.95845
[编辑]
没有插入符:
table(predict_mlogit,testing$V1)
# output
predict_mlogit 0 1 2 3 4 5 6 7 8 9
0 343 0 5 2 5 4 1 0 7 0
1 0 254 1 0 2 1 0 0 0 0
2 3 2 163 4 5 0 4 2 7 0
3 2 1 6 145 1 7 0 3 3 1
4 3 1 8 1 168 3 4 5 1 3
5 2 0 1 8 2 137 4 0 9 1
6 2 1 1 1 4 3 156 0 0 0
7 3 1 5 2 1 0 0 132 4 2
8 1 1 7 3 4 2 1 0 130 5
9 0 3 1 0 8 3 0 5 5 165
# accuracy
sum(predict_mlogit==testing$V1)/length(testing$V1)
# [1] 0.8933732
table(predict_lda$class,testing$V1)
# output
0 1 2 3 4 5 6 7 8 9
0 342 0 7 3 1 6 1 0 5 0
1 0 251 2 0 4 0 0 1 0 0
2 0 0 157 3 6 0 3 0 2 0
3 4 2 4 142 0 16 0 2 11 0
4 3 5 12 3 174 3 3 7 7 4
5 1 0 2 9 0 125 3 0 4 0
6 5 3 1 0 2 0 157 0 0 0
7 0 0 1 1 2 0 0 129 0 5
8 3 1 12 4 1 5 3 1 135 3
9 1 2 0 1 10 5 0 7 2 165
# accuracy
sum(predict_lda$class==testing$V1)/length(testing$V1)
# [1] 0.8854011
关于r - 比较测试性能，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42266481/