AstraZeneca/rexmex
A general purpose recommender metrics library for fair evaluation.
This project helps data scientists, machine learning engineers, and researchers fairly evaluate recommender systems. You input your system's predictions (e.g., predicted ratings, rankings, or classifications) and the actual outcomes. It then outputs a comprehensive report with various performance metrics and visualizations, allowing you to understand how well your recommender system works.
276 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to rigorously evaluate the performance of your recommender system and want a wide array of standardized metrics to assess its effectiveness.
Not ideal if you are looking to build a recommender system from scratch or deploy an existing one, as this tool focuses solely on evaluation.
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276
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25
Language
Python
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Last pushed
Aug 22, 2023
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