在作业中,要求我们对CART模型执行交叉验证。我尝试使用cvFit中的cvTools函数,但收到一条奇怪的错误消息。这是一个最小的示例:

library(rpart)
library(cvTools)
data(iris)
cvFit(rpart(formula=Species~., data=iris))


我看到的错误是:

Error in nobs(y) : argument "y" is missing, with no default


traceback()

5: nobs(y)
4: cvFit.call(call, data = data, x = x, y = y, cost = cost, K = K,
       R = R, foldType = foldType, folds = folds, names = names,
       predictArgs = predictArgs, costArgs = costArgs, envir = envir,
       seed = seed)
3: cvFit(call, data = data, x = x, y = y, cost = cost, K = K, R = R,
       foldType = foldType, folds = folds, names = names, predictArgs = predictArgs,
       costArgs = costArgs, envir = envir, seed = seed)
2: cvFit.default(rpart(formula = Species ~ ., data = iris))
1: cvFit(rpart(formula = Species ~ ., data = iris))


看来y对于cvFit.default是必需的。但:

> cvFit(rpart(formula=Species~., data=iris), y=iris$Species)
Error in cvFit.call(call, data = data, x = x, y = y, cost = cost, K = K,  :
  'x' must have 0 observations


我究竟做错了什么?哪个程序包可以使我不必自己编写CART树即可进行交叉验证? (我太懒了...)

最佳答案

插入符包使交叉验证变得轻而易举:

> library(caret)
> data(iris)
> tc <- trainControl("cv",10)
> rpart.grid <- expand.grid(.cp=0.2)
>
> (train.rpart <- train(Species ~., data=iris, method="rpart",trControl=tc,tuneGrid=rpart.grid))
150 samples
  4 predictors
  3 classes: 'setosa', 'versicolor', 'virginica'

No pre-processing
Resampling: Cross-Validation (10 fold)

Summary of sample sizes: 135, 135, 135, 135, 135, 135, ...

Resampling results

  Accuracy  Kappa  Accuracy SD  Kappa SD
  0.94      0.91   0.0798       0.12

Tuning parameter 'cp' was held constant at a value of 0.2

07-24 09:52
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