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由于 caret-ml 中有一些方法没有内置的特征选择，所以我正在试验rfe。

有人可以解释一下，除了训练时间和排名特征列表之外，train()和之间的区别是什么？rfe()事情是否只能train()做，反之亦然？

这是我的例子来说明这个问题：

# worker functions
useRFE <- function(trainctrl) {
    control <- rfeControl(functions=caretFuncs, 
    method="cv", 
    repeats=5, 
    number=5, 
    returnResamp="final", 
    verbose = TRUE)


 # run the RFE algorithm
 resultsRFE <-
    rfe(
       training[,1:ncol(iris) - 1], 
       training$Species, 
       sizes = subsets, 
       rfeControl = control,
       method = "svmRadial",
       metric = "Accuracy",
       trControl = trainctrl
    )

  # test
  predictedClasses <- predict(resultsRFE, testing)
  confmat <- confusionMatrix(data = predictedClasses$pred, reference = testing$Species)
  confmat
  # Balanced Accuracy = 100%
}

useTrain <-  function(trainctrl) {      
  # train
  resultsTrain <- train(
       Species ~ .,
       data = training,
       method = "svmRadial",
       metric = "Accuracy",
       trControl = trainctrl
  )

  # test
  predictedClasses <- predict(resultsTrain, testing)
  confmat <- confusionMatrix(data = predictedClasses, 
       reference = testing$Species)
  confmat
  # Balanced Accuracy = 100%
}
# load library
library(caret)

# load iris data
data(iris)

# create datasets
inTrain <- createDataPartition(y = iris$Species,
    p = .75,
    list = FALSE)

## The output is a set of integers for the rows of Sonar
## that belong in the training set.
training <<- iris[ inTrain,]
testing <<- iris[-inTrain,]

# select count of features
subsets <- c(1:ncol(iris)-1)

# set controls
trainctrl <- trainControl(
    classProbs= TRUE, 
    method="cv", 
    repeats=5, 
    number=5, 
    verbose = TRUE)

# compare
ptm <- proc.time()
useTrain(trainctrl = trainctrl)
proc.time() - ptm
# Accuracy : 0.9167    
# User      System     passed 
# 1.54      0.06       10.27

ptm <- proc.time()
useRFE(trainctrl = trainctrl)
proc.time() - ptm
# Accuracy : 0.9167
# User      System     passed 
# 0.86      0.04       24.44
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1 回答 1

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train 使用所有预测变量作为输入来构建和调整更多。rfe进行递归特征消除，其中以尝试优化模型性能的方式删除不同的预测变量。您可以rfe将train.

这些方法的解释在网站上。

于 2015-10-06T11:00:17.640 回答