caret虚拟人-vars排除目标

Question

如何在不破坏目标变量的情况下使用插入符号中的虚拟vars？

set.seed(5)
data <- ISLR::OJ
data<-na.omit(data)

dummies <- dummyVars( Purchase ~ ., data = data)
data2 <- predict(dummies, newdata = data)
split_factor = 0.5
n_samples = nrow(data2)
train_idx <- sample(seq_len(n_samples), size = floor(split_factor * n_samples))
train <- data2[train_idx, ]
test <- data2[-train_idx, ]
modelFit<- train(Purchase~ ., method='lda',preProcess=c('scale', 'center'), data=train)

将失败，因为缺少购买变量。如果我事先用data$Purchase <- ifelse(data$Purchase == "CH",1,0)替换它，插入符号抱怨这不再是一个分类而是一个回归问题。

Answer 1

至少在下面的注释中，示例代码似乎存在一些问题。回答你的问题：

• ifelse的结果是一个整数向量，而不是一个因子，因此训练函数默认为回归。
• 直接将dummyVars传递给函数的方法是使用列车(x =，y=，.)而不是公式

为了避免这些问题，请仔细检查对象的class。

请注意，preProcess选项在train()中将对所有数值变量(包括虚拟变量)应用预处理。下面的选项2避免了这一点，在调用train()之前对数据进行标准化。

set.seed(5)
data <- ISLR::OJ
data<-na.omit(data)

# Make sure that all variables that should be a factor are defined as such
newFactorIndex <- c("StoreID","SpecialCH","SpecialMM","STORE")
data[, newFactorIndex] <- lapply(data[,newFactorIndex], factor)

library(caret)
# See help for dummyVars. The function does not take a dependent variable and predict will give an error
# I don't include the target variable here, so predicting dummies on new data will drop unknown columns
# including the target variable
dummies <- dummyVars(~., data = data[,-1])
# I don't change the data yet to apply standardization to the numeric variables, 
# before turning the categorical variables into dummies

split_factor = 0.5
n_samples = nrow(data)
train_idx <- sample(seq_len(n_samples), size = floor(split_factor * n_samples))

# Option 1 (as asked): Specify independent and dependent variables separately
# Note that dummy variables will be standardized by preProcess as per the original code

# Turn the categorical variabels to (unstandardized) dummies
# The output of predict is a matrix, change it to data frame
data2 <- data.frame(predict(dummies, newdata = data))

modelFit<- train(y = data[train_idx, "Purchase"], x = data2[train_idx,], method='lda',preProcess=c('scale', 'center'))

# Option 2: Append dependent variable to the independent variables (needs to be a data frame to allow factor and numeric)
# Note that I also shift the proprocessing away from train() to
# avoid standardizing the dummy variables 

train <- data[train_idx, ]
test <- data[-train_idx, ]

preprocessor <- preProcess(train[!sapply(train, is.factor)], method = c('center',"scale"))
train <- predict(preprocessor, train)
test <- predict(preprocessor, test)

# Turn the categorical variabels to (unstandardized) dummies
# The output of predict is a matrix, change it to data frame
train <- data.frame(predict(dummies, newdata = train))
test <- data.frame(predict(dummies, newdata = test))

# Reattach the target variable to the training data that has been 
# dropped by predict(dummies,...)
train$Purchase <- data$Purchase[train_idx]
modelFit<- train(Purchase ~., data = train, method='lda')