# Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets

arXiv: https://arxiv.org/abs/1803.05127 · HTML: https://mlsanity.com/paper/1803.05127/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Scale-Invariant Learning-to-Rank](https://mlsanity.com/paper/2410.01959/) (2024)

## Abstract (arXiv)

With the rapid advance of the Internet, search engines (e.g., Google, Bing, Yahoo!) are used by billions of users for each day. The main function of a search engine is to locate the most relevant webpages corresponding to what the user requests. This report focuses on the core problem of information retrieval: how to learn the relevance between a document (very often webpage) and a query given by user. Our analysis consists of two parts: 1) we use standard statistical methods to select important features among 137 candidates given by information retrieval researchers from Microsoft. We find that not all the features are useful, and give interpretations on the top-selected features; 2) we give baselines on prediction over the real-world dataset MSLR-WEB by using various learning algorithms. We find that models of boosting trees, random forest in general achieve the best performance of prediction. This agrees with the mainstream opinion in information retrieval community that tree-based algorithms outperform the other candidates for this problem.