evalModel: Creating the evaluation model.

In rrecsys: Environment for Evaluating Recommender Systems

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 _ds.
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, _ds.

Examples

x <- matrix(sample(c(0:5), size = 200, replace = TRUE, 
     prob = c(.6,.08,.08,.08,.08,.08)), nrow = 20, byrow = TRUE)

d <- defineData(x)
     
my_2_folds <- evalModel(d, 2)             #output class evalModel.

my_2_folds
# 2 - fold cross validation model on the dataset with 20 users and 10 items.

my_2_folds@data                     #the dataset.
my_2_folds@folds                    #the number of folds in the model.
my_2_folds@fold_indices               #the index of each item in the fold.
     

rrecsys documentation built on June 10, 2019, 1:02 a.m.