evalml.pipelines.components.XGBoostRegressor.__init__¶
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XGBoostRegressor.__init__(eta=0.1, max_depth=6, min_child_weight=1, n_estimators=100, random_seed=0, n_jobs=- 1, **kwargs)[source]¶ XGBoost Regressor.
- Parameters
eta (float) – Learning rate. Defaults to 0.1.
max_depth (int) – Maximum tree depth for base learners. Defaults to 6.
min_child_weight (float) – Minimum sum of instance weight(hessian) needed in a child. Defaults to 1.
n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100.
random_seed (int) – Seed for the random number generator. Defaults to 0.
n_jobs (int) – Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to -1.