Choosing the Right Ranking Metric

Advanced Ranking Modes: YetiRankPairwise

The best metric for your task depends on your specific needs. Here are some factors to consider:

Relevance vs. Position: If precise ranking of highly relevant items is crucial, NDCG or MAP might be better choices.
Number of Relevant Items: For scenarios with a high number of relevant items, HR might be more informative.
Business Goals: Align the metric with your business objectives. For example, if click-through rate is essential, consider incorporating user interaction data into a custom metric.

CatBoost Ranking Metrics: A Comprehensive Guide

CatBoost, short for “Categorical Boosting,” is a powerful gradient boosting library developed by Yandex. It is renowned for its efficiency, accuracy, and ability to handle categorical features with ease. One of the key features of CatBoost is its support for ranking tasks, which are crucial in applications like search engines, recommendation systems, and information retrieval. This article delves into the various ranking metrics supported by CatBoost, their usage, and how they can be leveraged to build high-performing ranking models.

Table of Content

Understanding Ranking in CatBoost
Key CatBoost Ranking Metrics

1. Normalized Discounted Cumulative Gain (NDCG)
2. Mean Reciprocal Rank (MRR)
3. Expected Reciprocal Rank (ERR)
4. Mean Average Precision (MAP)