classification

Classification recommendations metrics.

Functions

calc_classification_metrics(metrics, merged)	Calculate any classification metrics.
calc_confusions(merged, k)	Calculate some intermediate metrics from prepared data (it's a helper function).
make_confusions(reco, interactions, k)	Calculate some intermediate metrics from raw data (it's a helper function).

Classes

Accuracy(k)	Ratio of correctly recommended items among all items.
ClassificationMetric(k)	Classification metric base class.
F1Beta(k[, beta])	Fbeta score for k first recommendations.
HitRate(k)	HitRate calculates the fraction of users for which the correct answer is included in the recommendation list.
MCC(k)	Matthew correlation coefficient calculates correlation between actual and predicted classification.
Precision(k)	Ratio of relevant items among top-k recommended items.
Recall(k)	Ratio of relevant recommended items among all items user interacted with after recommendations were made.
SimpleClassificationMetric(k)	Simple classification metric base class.