cvKfold: K-fold Cross Validation
In SkadiEye/ITRlearn: Individualized Treatment Rule Learning

Description Usage Arguments Value See Also

Randomly cut the training set into k-folds and Conduct a K-fold Cross Validation. A list of models can be evaluated in the same function. The performance for each model will be evaluated as required.

1 2	cvKfold(trainObj, k.fold = 10, cvCtrl.list, cutoff = 0.5, unbalance.trt = c("None", "Over", "Under")[1], final.models = FALSE)

`trainObj`	A `RegObj` or `wClsObj` containing the training set.
`k.fold`	The number of folds in the cross validation.
`cvCtrl.list`	A `list` of `list`s of arguments that specifies a regression or classification method. Each list of arguments is handled by `itrCtrlPanel`.
`cutoff`	Cutoff used when predicting the current fold that not used in training the model, passed to `cutoff.calc` function.
`unbalance.trt`	A character string that specifies if sampleing techniques should be applied to data with unbalanced numbers for each class.
`final.models`	A `logical` value indicating whether the final models are kept in the outout.