Question 1

How to evaluate a recommender system with rexmex?

Accepted Answer

Install rexmex via pip, then use metric sets like ClassificationMetricSet with ScoreCard to compute metrics on your true labels and predicted scores. The README provides examples with synthetic datasets for binary labels and probability scores.

Question 2

rexmex vs scikit-learn for recommender metrics?

Accepted Answer

RexMex specializes in recommender system metrics, including ranking and coverage measures not in scikit-learn, which focuses on general ML metrics. Use rexmex for comprehensive recsys evaluation with pre-configured sets, while scikit-learn is better for basic classification or regression tasks.

Question 3

How to calculate NDCG using rexmex?

Accepted Answer

Use the RankingMetricSet, which includes NDCG, and pass your true rankings and predicted scores to the ScoreCard for computation. The metric is listed under the ranking set in the README with links to documentation.

Question 4

Does rexmex support fairness evaluation for recommender systems?

Accepted Answer

Yes, through grouped evaluation by attributes like source or target groups, allowing analysis of performance across different user or item segments. This is shown in the advanced example for bias insights.

Question 5

Can I add custom metrics to rexmex?

Accepted Answer

The library is extensible; you can subclass MetricSet and implement custom metrics. Check the documentation for guidelines, and note that the README encourages feature requests for missing methods.

Question 6

Is rexmex suitable for model monitoring in production?

Accepted Answer

It excels at offline evaluation and batch analysis due to comprehensive metric calculation, but may be heavy for real-time monitoring; consider using it for periodic assessments rather than continuous streaming.

RexMex

What is RexMex?

Overview

Use Cases

Best For

Related Projects

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions