rrecsys (version 0.9.5.4)

evalModel: Creating the evaluation model.

Description

Creates the dataset split for evaluation where ratings of each user are uniformly distributed over k random folds. The function returns the list of items that are assigned to each fold, such that algorithms can be compared on the same train/test splits.

Usage

evalModel(data, folds)

Arguments

data

dataset, of class dataSet.

folds

The number of folds to use in the k-fold cross validation, of class numeric, default value set to 5.

Value

An object of class evalModel-class.

See Also

evalModel-class, evalRec, dataSet.

Examples

# NOT RUN {
x <- matrix(sample(c(0:5), size = 200, replace = TRUE, 
     prob = c(.6,.08,.08,.08,.08,.08)), nrow = 20, byrow = TRUE)

x <- defineData(x)
     
my_10_folds <- evalModel(x, 10)             #output class evalModel.

my_6_folds <- evalModel(x, 6)  

my_6_folds
#6 - fold cross validation model on the dataset with 20 users and 10 items.

my_6_folds@data                     #the dataset.
my_6_folds@folds                    #the number of folds in the model.
my_6_folds@fold_indices               #the index of each item in the fold.
     
# }