我注意到当使用glmnet包在插入符号中运行惩罚逻辑回归时，模型预测被重新分类为0或1个结果：
mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
train_control <- trainControl(method="cv", number=10, savePredictions = TRUE)
glmnetGrid <- expand.grid(alpha=c(0, .5, 1), lambda=c(.1, 1, 10))
model<- train(as.factor(admit) ~ ., data=mydata, trControl=train_control, method="glmnet", family="binomial", tuneGrid=glmnetGrid, metric="Accuracy", preProcess=c("center","scale"))
model
glmnet
400 samples
3 predictor
2 classes: '0', '1'
Pre-processing: centered (3), scaled (3)
Resampling: Cross-Validated (10 fold)
Summary of sample sizes: 360, 360, 361, 359, 360, 361, ...
Resampling results across tuning parameters:
alpha lambda Accuracy Kappa Accuracy SD Kappa SD
0.0 0.1 0.6923233271 0.09027099758 0.018975211636 0.06988057154
0.0 1.0 0.6825703565 0.00000000000 0.007557700521 0.00000000000
0.0 10.0 0.6825703565 0.00000000000 0.007557700521 0.00000000000
0.5 0.1 0.6825703565 0.00000000000 0.007557700521 0.00000000000
0.5 1.0 0.6825703565 0.00000000000 0.007557700521 0.00000000000
0.5 10.0 0.6825703565 0.00000000000 0.007557700521 0.00000000000
1.0 0.1 0.6825703565 0.00000000000 0.007557700521 0.00000000000
1.0 1.0 0.6825703565 0.00000000000 0.007557700521 0.00000000000
1.0 10.0 0.6825703565 0.00000000000 0.007557700521 0.00000000000
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were alpha = 0 and lambda = 0.1.
> head(model$pred)
pred obs rowIndex alpha lambda Resample
1 0 0 16 0 10 Fold01
2 0 0 17 0 10 Fold01
3 0 0 24 0 10 Fold01
4 0 1 46 0 10 Fold01
5 0 0 69 0 10 Fold01
6 0 0 84 0 10 Fold01
> summary(model$pred)
pred obs rowIndex alpha lambda Resample
0:3576 0:2457 Min. : 1.00 Min. :0.0 Min. : 0.1 Length:3600
1: 24 1:1143 1st Qu.:100.75 1st Qu.:0.0 1st Qu.: 0.1 Class :character
Median :200.50 Median :0.5 Median : 1.0 Mode :character
Mean :200.50 Mean :0.5 Mean : 3.7
3rd Qu.:300.25 3rd Qu.:1.0 3rd Qu.:10.0
Max. :400.00 Max. :1.0 Max. :10.0
是否有可能获得原始预测概率= exp（logit（y））而不是0/1预测结果？