Understanding Ranking in CatBoost
Ranking metrics often focus on the performance of the model on the top positions (e.g., top 10) of the retrieved results. CatBoost allows you to specify the number of top positions (k) to consider when calculating the metric. Ranking tasks involve ordering items in a list based on their relevance to a particular query. CatBoost provides several ranking modes and metrics to optimize and evaluate ranking models. The primary ranking modes in CatBoost include:
- YetiRank
- PairLogit
- QuerySoftmax
- QueryRMSE
- YetiRankPairwise
- PairLogitPairwise
These modes can be used on both CPU and GPU, with some additional modes like YetiRankPairwise
and PairLogitPairwise
available for more complex ranking tasks.
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)
- Advanced Ranking Modes: YetiRankPairwise
- Choosing the Right Ranking Metric
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