An evaluation scheme created from a data set. The scheme can be a simple split into training and test data, k-fold cross-evaluation or using k bootstrap samples.
Objects can be created by
evaluationScheme(data, method="split", train=0.9, k=NULL, given=3).
data:Object of class "ratingMatrix"; the data set.
given:Object of class "integer"; given ratings are
randomly selected for each evaluation user and
presented to the recommender
algorithm to calculate recommend items/ratings.
The recommended items are compared
to the remaining items for the evaluation user.
goodRating:Object of class "numeric"; Rating at which an item is considered a positive for evaluation.
k:Object of class "integer"; number of runs for evaluation. Default is 1 for method "split" and 10 for "cross-validation" and "bootstrap".
knownData:Object of class "ratingMatrix"; data set with only known (given) items.
method:Object of class "character"; evaluation method. Available methods are: "split", "cross-validation" and "bootstrap".
runsTrain:Object of class "list"; internal repesentation for the split in training and test data for the evaluation runs.
train:Object of class "numeric"; portion of data used for training for "split" and "bootstrap".
unknownData:Object of class "ratingMatrix"; data set with only unknown items.
signature(x = "evaluationScheme"): access data.
Parameters are type ("train", "known" or "unknown", "given") and
run (1...k).
"train" returns the training data for the run,
"known" returns the known ratings used for prediction
for the test data,
"unknown" returns the ratings used for evaluation
for the test data, and
"given" returns the number of items that were given in "known." If the given items
was a positive number, then this will be a vector with this number, but if given was negative (all-but-x),
then the number of given items for each test user will be different.
signature(object = "evaluationScheme")
ratingMatrix and
the creator function evaluationScheme.