rminer (version 1.4.1)

crossvaldata: Computes k-fold cross validation for rminer models.

Description

Computes k-fold cross validation for rminer models.

Usage

crossvaldata(x, data, theta.fit, theta.predict, ngroup = 10,  mode = "stratified", seed = NULL, model, task, feature = "none", ...)

Arguments

x

See fit for details.

data

See fit for details.

theta.fit

fitting function

theta.predict

prediction function

ngroup

number of folds

mode

Possibilities are: "stratified", "random" or "order" (see holdout for details).

seed

if NULL then a random seed is used; else a fixed seed is adopted (will return always the same result for the same seed).

model

See fit for details.

task

See fit for details.

feature

See fit for details.

...

Additional parameters sent to theta.fit or theta.predic (e.g. search)

Value

Returns a list with:

• $cv.fit -- all predictions (factor if task="class", matrix if task="prob" or numeric if task="reg");
• $mpar -- matrix with the mpar for each fold;
• $attributes -- the selected attributes for each fold if a feature selection algorithm was adopted;
• $ngroup -- the number of folds;
• $leave.out -- the computed size for each fold (=nrow(data)/ngroup);
• $groups -- vector list with the indexes of each group;
• $call -- the call of this function;

Details

Standard k-fold cross-validation adopted for rminer models. By default, for classification tasks ("class" or "prob") a stratified sampling is used (the class distributions are identical for each fold), unless mode is set to random or order (see holdout for details).

References

Check the crossval function of the bootstrap library.

See Also

holdout, fit, mining and predict.fit.

Examples

 data(iris)
 # 3-fold cross validation using fit and predict
 # the control argument is sent to rpart function 
 # rpart.control() is from the rpart package
 M=crossvaldata(Species~.,iris,fit,predict,ngroup=3,seed=12345,model="rpart",
                task="prob", control = rpart::rpart.control(cp=0.05))
 print("cross validation object:")
 print(M)
 C=mmetric(iris$Species,M$cv.fit,metric="CONF")
 print("confusion matrix:")
 print(C)