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
和
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