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 Datasets can be read from input files. For example, the Pool class offers this functionality.

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

# Apply model on pool with baseline values preds1 = catboost_model.predict(test_pool) # Apply model on numpy.ndarray and then add the baseline values preds2 = …

If this parameter is not None and the training dataset passed as the value of the X parameter to the fit function of this class has the catboost.Pool type, CatBoost checks the equivalence of …

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 releases page on GitHub.

As of CatBoost 1.2.8, devices with CUDA compute capability >= 3.5 are supported in released packages. All necessary CUDA libraries are statically linked in the released Linux and …

CatBoost class CatBoost (params= None) Purpose Training and applying models. Note There are compatibility issues with Scikit-learn 1.8.x. See this GitHub issue for details. Parameters …

If any features in the cat_features parameter are specified as names instead of indices, feature names must be provided for the training dataset. Therefore, the type of the X parameter in the …