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.

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

# 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 = baseline_model.predict(X_test) + …

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 params …

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 future calls of …

CatBoost is based on gradient boosted decision trees. During training, a set of decision trees is built consecutively. Each successive tree is built with reduced loss compared to the previous trees. The …

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 future calls of …

When the value of the leaf_estimation_iterations parameter is greater than 1, CatBoost makes several gradient or newton steps when calculating the resulting leaf values of a tree.