CatBoost is a decision tree gradient boosting machine learning algorithm. Currently, it is available as an open-source library. It was developed by Yandex researchers and engineers and used by Yandex and other organizations such as CERN, Cloudflare, and Careem taxi for search, recommendation systems, personal assistant, self-driving cars, weather prediction, and many other activities. Anyone can use it because it is open-source. Its distinctive features and latest advancements include great quality without parameter tuning, categorical features support, implementation of ordered boosting, fast and scalable GPU version, missing value support, great visualization, improved accuracy and quick predictions. CatBoost is an excellent solution for heterogeneous data problems, but it might not be the best learner for cases that deal with homogenous data. Preprocessing, prediction time, and model analysis are among Catboost’s strengths, whereas its training and optimization times constitute its weaknesses.
For documentation and issues, visit: https://github.com/catboost/catboost