You have seen how gradient boosting models excel at standard regression and classification problems. However, their flexibility extends to more specialized machine learning tasks. This chapter demonstrates how to apply and adapt gradient boosting frameworks to solve problems beyond the typical supervised learning setup.

You will learn about:

• Learning to Rank: Applying gradient boosting to order items based on relevance, often used in search and recommendation systems. We will look at specific objective functions like rank:pairwise.
• Survival Analysis: Using gradient boosting to model time-to-event data, accounting for censored observations common in medical studies or failure analysis. This involves objectives like the Cox Proportional Hazards model.
• Quantile Regression: Estimating specific conditional quantiles of the target variable, providing a more complete picture of the distribution than just predicting the mean. This requires implementing or using quantile loss functions.
• Multi-Output Problems: Strategies for handling scenarios where each input needs to predict multiple target variables simultaneously.

We will examine the necessary modifications to the objective functions and configurations required to effectively use XGBoost, LightGBM, or CatBoost for these specific applications. The chapter concludes with a practical implementation of a learning-to-rank task.