  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.
