NReco.CF.Taste.Impl.Eval NamespaceNReco.Recommender Class Library

Public classAbstractDifferenceRecommenderEvaluator
Public classAverageAbsoluteDifferenceRecommenderEvaluator
A IRecommenderEvaluator which computes the average absolute difference between predicted and actual ratings for users.
Public classGenericRecommenderIRStatsEvaluator
For each user, these implementation determine the top preferences, then evaluate the IR statistics based on a IDataModel that does not have these values. This number is the "at" value, as in "precision at 5". For example, this would mean precision evaluated by removing the top 5 preferences for a user and then finding the percentage of those 5 items included in the top 5 recommendations for that user.
Public classGenericRelevantItemsDataSplitter
Picks relevant items to be those with the strongest preference, and includes the other users' preferences in full.
Public classIRStatisticsImpl
Public classLoadCallable
Public classLoadEvaluator
Simple helper class for running load on a Recommender.
Public classLoadStatistics
Public classOrderBasedRecommenderEvaluator
Evaluate recommender by comparing order of all raw prefs with order in recommender's output for that user. Can also compare data models.
Public classRMSRecommenderEvaluator
A IRecommenderEvaluator which computes the "root mean squared" difference between predicted and actual ratings for users. This is the square root of the average of this difference, squared.