Boosted trees for ranking

Boosted trees for ranking are a machine learning method that builds a series of decision trees topredict the most relevant items for a user. Each tree focuses on errors made by previous trees, gradually improving accuracy. By combining many small, simple trees into a strong overall model, boosted trees can effectively identify the best order of results in search engines or recommendation systems. This approach enhances ranking quality by learning complex patterns in data, making the system better at predicting what users will find most useful or interesting.