CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box, successor of the MatrixNet algorithm developed by Yandex.

CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. It is available as an open source library.

CatBoost is well covered with educational materials for both novice and advanced machine learners and data scientists. Video tutorial.

class CatBoost (params= None ). Purpose. Training and applying models. Parameters params Description. The list of parameters to start training with.

An up-to-date list of available CatBoost releases and the corresponding binaries for different operating systems is available in the Download section of the rel

Use one of the following examples after installing the Python package to get started: CatBoostClassifier. CatBoostRegressor. CatBoost. CatBoostClassifier.

Implementation of the scikit-learn estimator API for CatBoost classification. Supports model training, inference and auxiliary calculations like feature importance.

Regression CatBoostRegressor class with array-like data.

Purpose Implementation of the scikit-learn estimator API for CatBoost regression. Supports model training, inference and auxiliary calculations like feature importance. Parameters metadata …

The built versions of CatBoost have GPU support out-of-the-box. As of CatBoost 1.2.10, devices with CUDA compute capability >= 3.5 are supported in released packages. All necessary CUDA libraries …