我正在尝试使用这里的一些数据并比较glm和lda的测试性能。
数据附于此处。
这是我尝试做这两件事的总体计划：
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)
然而这些都行不通。我认为有一个多项式的家庭类，但似乎并不存在。此外，由于我的第一列是数字而下一个是灰度值，我认为我做V1 ~ .，但我认为这些情况也不正确。有没有人知道我的语法/设置是否错误？
编辑：我添加了我如何计算LDA的分类错误。但是我不认为我原来的东西是有效的，因为它给出了：
（pred_lda）中的错误 - 测试$ V1：二元运算符的非数字参数
答案 0 :(得分：1)
这不是二元分类，而是一个多类（数字）分类问题，我们有10个类标签。因此，您需要使用多项logit，而不是逻辑回归。如我们所见，尝试以下内容，多项logit模型的预测总体准确度高于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$V1)
#output
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3 4 5 6 7 8 9
0 342 0 7 3 1 6 1 0 5 0
1 0 251 2 0 4 0 0 1 0 0
2 0 0 157 3 6 0 3 0 2 0
3 4 2 4 142 0 16 0 2 11 0
4 3 5 12 3 174 3 3 7 7 4
5 1 0 2 9 0 125 3 0 4 0
6 5 3 1 0 2 0 157 0 0 0
7 0 0 1 1 2 0 0 129 0 5
8 3 1 12 4 1 5 3 1 135 3
9 1 2 0 1 10 5 0 7 2 165
Overall Statistics
Accuracy : 0.8854
95% CI : (0.8706, 0.899)
No Information Rate : 0.1789
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.8713
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8 Class: 9
Sensitivity 0.9526 0.9508 0.79293 0.85542 0.87000 0.78125 0.92353 0.87755 0.81325 0.93220
Specificity 0.9860 0.9960 0.99226 0.97882 0.97399 0.98971 0.99401 0.99516 0.98207 0.98470
Pos Pred Value 0.9370 0.9729 0.91813 0.78453 0.78733 0.86806 0.93452 0.93478 0.80357 0.85492
Neg Pred Value 0.9896 0.9926 0.97767 0.98686 0.98544 0.98121 0.99293 0.99037 0.98314 0.99338
Prevalence 0.1789 0.1315 0.09865 0.08271 0.09965 0.07972 0.08470 0.07324 0.08271 0.08819
Detection Rate 0.1704 0.1251 0.07823 0.07075 0.08670 0.06228 0.07823 0.06428 0.06726 0.08221
Detection Prevalence 0.1819 0.1286 0.08520 0.09018 0.11011 0.07175 0.08371 0.06876 0.08371 0.09616
Balanced Accuracy 0.9693 0.9734 0.89260 0.91712 0.92200 0.88548 0.95877 0.93636 0.89766 0.95845
<强> [编辑]
没有caret：
table(predict_mlogit,testing$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