evalml.pipelines.components.CatBoostRegressor.__init__

CatBoostRegressor.__init__(n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=False, allow_writing_files=False, random_seed=0, n_jobs=- 1, **kwargs)[source]

CatBoost Regressor.

Parameters

  • n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100.

  • eta (float) – Learning rate. Defaults to 0.1.

  • max_depth (int) – Maximum tree depth for base learners. Defaults to 6.

  • bootstrap_type (string) – Defines the method for sampling the weights of objects. Defaults to None.

  • silent (bool) – Whether to emit logging while training. Default to False.

  • allow_writing_files (bool) – Whether to allow writing of analytical and snapshot files during training. Defaults to False.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

  • n_jobs (int) – Number of parallel threads used to run CatBoost. This will be passed to CatBoost as the thread_count parameter. Defaults to -1.

evalml.pipelines.components.CatBoostRegressor evalml.pipelines.components.CatBoostRegressor.clone