evaluationScheme {recommenderlab}R Documentation

Create an Evaluation Scheme

Description

Creates from a data set an evaluation scheme. The scheme can be a simple split into training and test data, k-fold cross-evaluation or using k bootstrap samples.

Usage

evaluationScheme(data, ...)
## S4 method for signature 'ratingMatrix':
evaluationScheme(data, method="split", train=0.9, 
k=10, given=3)

Arguments

data data set.
method a character string defining the recommender method to use (see details).
train fraction of the data set used for training.
k number of folds/times to run the evaluation.
given single number of items given for evaluation or a vector of length of data giving the number of items given for each observation.
... further arguments.

Details

evaluationScheme creates an evaluation scheme with k runs following the given method:

"split" randomly assigns the proportion of objects given by train to the training set and the rest is used for the test set.

"cross-validation" creates a k-fold cross-validation scheme. The data is randomly split into k parts and in each run k-1 parts are used for training and the remaining part is used for testing. After all runs each part was used as test set once.

"bootstrap" creates the training set by taking a bootstrap sample (sampling with replacement) of size train times size of the data set. All objects not in the training set are used for testing.

Value

Returns an object of class "evaluationScheme".

References

Kohavi, Ron (1995). "A study of cross-validation and bootstrap for accuracy estimation and model selection". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1137-1143.

See Also

evaluationScheme, ratingMatrix.

Examples

data("MSWeb")

MSWeb10 <- sample(MSWeb[rowCounts(MSWeb) >10,], 100)
MSWeb10 

esSplit <- evaluationScheme(MSWeb10, method="split",
        train = 0.9, k=4, given=3)
esSplit

esCross <- evaluationScheme(MSWeb10, method="cross-validation",
        k=4, given=3)
esCross

[Package recommenderlab version 0.1-0 Index]