Gradient Boosting is a powerful machine learning technique that achieves state-of-the-art results in a variety of practical tasks. Even in the era of deep learning, it is the primary method for learning problems with heterogeneous features, noisy data, and complex dependencies: web search, fraud detection, weather forecasting, and many others.

In this talk we will cover the CatBoost Gradient Boosting library. We will discuss common use cases for Gradient Boosting, what types of data it can be applied to, and a particular application to machine learning problems where data can be represented in the form of a graph of pairwise relations between samples.

SPEAKERS: