7

您好，提前致谢。我caret用来交叉验证包中的神经网络nnet。在函数的method参数中，trainControl我可以指定我的交叉验证类型，但所有这些都随机选择观察结果进行交叉验证。无论如何，我可以使用插入符号通过 ID 或硬编码参数对数据中的特定观察结果进行交叉验证吗？例如，这是我当前的代码：

library(nnet) 
library(caret) 
library(datasets) 

data(iris) 

train.control <- trainControl( 
    method = "repeatedcv" 
    , number = 4 
    , repeats = 10 
    , verboseIter = T 
    , returnData = T 
    , savePredictions = T 
    ) 

tune.grid <- expand.grid( 
    size = c(2,4,6,8)
    ,decay = 2^(-3:1) 
    ) 

nnet.train <- train( 
    x = iris[,1:4] 
    , y = iris[,5] 
    , method = "nnet" 
    , preProcess = c("center","scale")  
    , metric = "Accuracy" 
    , trControl = train.control 
    , tuneGrid = tune.grid 
    ) 
nnet.train 
plot(nnet.train)

假设我想CV_GROUP在iris数据框中添加另一列，并且我希望插入符号交叉验证神经网络的观察值，1该列的值为： 

iris$CV_GROUP <- c(rep.int(0,times=nrow(iris)-20), rep.int(1,times=20))

这可能caret吗？

4

1 回答 1

8

使用index和indexOut控制选项。我编写了一种实现方式，让您可以选择所需的重复次数和折叠次数：

library(nnet)
library(caret)
library(datasets)
library(data.table)
library(e1071)

r <- 2 # number of repeats
k <- 5 # number of folds
data(iris)
iris <- data.table(iris)

# Create folds and repeats here - you could create your own if you want #
set.seed(343)
for (i in 1:r) {
    newcol <- paste('fold.num',i,sep='')
    iris <- iris[,eval(newcol):=sample(1:k, size=dim(iris)[1], replace=TRUE)]
}

folds.list.out <- list()
folds.list <- list()
list.counter <- 1
for (y in 1:r) {
    newcol <- paste('fold.num', y, sep='')
    for (z in 1:k) {
        folds.list.out[[list.counter]] <- which(iris[,newcol,with=FALSE]==z)
        folds.list[[list.counter]] <- which(iris[,newcol,with=FALSE]!=z)
        list.counter <- list.counter + 1
    }
    iris <- iris[,!newcol,with=FALSE]
}

tune.grid <- expand.grid( 
    size = c(2,4,6,8)
    ,decay = 2^(-3:1) 
    ) 

train.control <- trainControl( 
    index=folds.list
    , indexOut=folds.list.out
    , verboseIter = T 
    , returnData = T 
    , savePredictions = T 
    ) 

iris <- data.frame(iris)

nnet.train <- train( 
    x = iris[,1:4] 
    , y = iris[,5] 
    , method = "nnet" 
    , preProcess = c("center","scale")  
    , metric = "Accuracy" 
    , trControl = train.control 
    , tuneGrid = tune.grid 
    ) 

nnet.train
plot(nnet.train)
于 2015-08-18T18:01:13.363 回答