Question

因为don't have a built in feature selection的插入符号ml中有一些方法可用，我正在尝试使用rfe。

有人可以解释一下，除了培训时间和排名功能列表之外， train()和rfe()之间的区别是什么？事情只有train()可以做，反之亦然吗？

以下是我举例说明问题：

# 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

Answer 1

train builds and tunes将所有预测变量作为输入。 rfe在尝试优化模型性能的方式中以different predictors are removed进行递归功能消除。您可以将rfe视为train的可能包装。

这些方法的解释在website上。

R插入：火车和rfe之间的区别

1 个答案: