R:基于Logistic回归的交叉验证特征选择

Question

我目前正在学习如何在R中实施后勤倒退。

我使用了一个数据集并将其划分为一个培训和测试集，并希望使用交叉验证来实现forward selection、backward selection和best subset selection，以选择最佳的特性。我使用caret在训练数据集上实现cross-validation，然后对测试数据进行预测测试。

我在插入符号中看到了rfe控件，并查看了caret 网站上的文档以及问题如何在R中使用包装器特征选择和算法？上的链接。我并不清楚如何更改特性选择的类型，因为它似乎默认为向后选择。有人能帮我处理我的工作流程吗。下面是一个可重复的例子

library("caret")

# Create an Example Dataset from German Credit Card Dataset
mydf <- GermanCredit

# Create Train and Test Sets 80/20 split
trainIndex <- createDataPartition(mydf$Class, p = .8, 
                              list = FALSE, 
                              times = 1)

train <- mydf[ trainIndex,]
test  <- mydf[-trainIndex,]


ctrl <- trainControl(method = "repeatedcv", 
                 number = 10, 
                 savePredictions = TRUE)

mod_fit <- train(Class~., data=train, 
             method="glm", 
             family="binomial",
             trControl = ctrl, 
             tuneLength = 5)


# Check out Variable Importance
varImp(mod_fit)
summary(mod_fit)

# Test the new model on new and unseen Data for reproducibility
pred = predict(mod_fit, newdata=test)
accuracy <- table(pred, test$Class)
sum(diag(accuracy))/sum(accuracy)

Answer 1

您可以简单地在mod_fit中调用它。当谈到向后逐步的时候，下面的代码就足够了

trControl <- trainControl(method="cv",
                          number = 5,
                          savePredictions = T,
                          classProbs = T,
                          summaryFunction = twoClassSummary)

caret_model <- train(Class~.,
                     train,
                     method="glmStepAIC", # This method fits best model stepwise.
                     family="binomial",
                     direction="backward", # Direction
                     trControl=trControl)

注意，在trControl中

method= "cv", # No need to call repeated here, the number defined afterward defines the k-fold.
classProbs = T,
summaryFunction = twoClassSummary # Gives back ROC, sensitivity and specifity of the chosen model.