evalRecResults: Evaluation results.

In ludovikcoba/rrecsys: Environment for Evaluating Recommender Systems

Evaluation results.

Description

Defines a structure for the results obtained by evaluating an algorithm

Slots

data:

class "_ds", the dataset.

alg:

class "character", the name of the used algorithm.

topN:

class "numeric", the number N of Top-N items recommended to each user.

topNGen:

class "character", the name of the recommendation algorithm.

positiveThreshold:

class "numeric", indicating the threshold of the ratings to be considered a good. This attribute is not used when evaluating implicit feedback.

alpha:

class numeric, is the half-life parameter for the rankscore metric.

parameters:

class "list", parameters used in the configuration of the algorithm.

TP:

class "numeric", True Positives count on each fold.

FP:

class "numeric", False Positives count on each fold.

TN:

class "numeric", True Negatives count on each fold.

FN:

class "numeric", False Negatives count on each fold.

precision:

class "numeric", precision measured on each fold.

recall:

class "numeric", recall measured on each fold.

F1:

class "numeric", F1 measured on each fold.

nDCG:

class "numeric", nDCG measured on each fold.

rankscore:

class "numeric", rankscore measured on each fold.

item_coverage:

class "numeric", item coverage.

user_coverage:

class "numeric", user coverage.

ex.time:

class "numeric", the execution time.

TP_count:

class "numeric", True positives count on each item.

rec_counts:

class "numeric", counts how many times an item was recommended.

rec_popularity:

class "numeric", popularity of recommendations.

Methods

show

signature(object = "evalRecResults")

results

signature(object = "evalRecResults", metrics = "character"): returns a subset of the results based on the required metric.

ludovikcoba/rrecsys documentation built on May 17, 2022, 2:33 a.m.