bigframes.ml.ensemble.RandomForestRegressor#

class bigframes.ml.ensemble.RandomForestRegressor(n_estimators: int = 100, *, tree_method: Literal['auto', 'exact', 'approx', 'hist'] = 'auto', min_tree_child_weight: int = 1, colsample_bytree: float = 1.0, colsample_bylevel: float = 1.0, colsample_bynode: float = 0.8, gamma: float = 0.0, max_depth: int = 15, subsample: float = 0.8, reg_alpha: float = 0.0, reg_lambda: float = 1.0, tol: float = 0.01, enable_global_explain: bool = False, xgboost_version: Literal['0.9', '1.1'] = '0.9')[source]#

A random forest regressor.

A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

Parameters:

  • n_estimators (Optional[int]) – Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2.

  • tree_method (Optional[str]) – Specify which tree method to use. Default to “auto”. If this parameter is set to default, XGBoost will choose the most conservative option available. Possible values: “exact”, “approx”, “hist”.

  • min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child. Default to 1.

  • colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree. Default to 1.0. The value should be between 0 and 1.

  • colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1.

  • colsample_bynode (Optional[float]) – Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1.

  • gamma (Optional[float]) – (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree. Default to 0.0.

  • max_depth (Optional[int]) – Maximum tree depth for base learners. Default to 15. The value should be greater than 0 and less than 1.

  • (Optional[float] (subsample) – Subsample ratio of the training instance. Default to 0.8. The value should be greater than 0 and less than 1.

  • reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha). Default to 0.0.

  • reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda). Default to 1.0.

  • tol (Optional[float]) – Minimum relative loss improvement necessary to continue training. Default to 0.01.

  • enable_global_explain (Optional[bool]) – Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False.

  • xgboost_version (Optional[str]) – Specifies the Xgboost version for model training. Default to “0.9”. Possible values: “0.9”, “1.1”.

predict(X: DataFrame | Series | DataFrame | Series) → DataFrame[source]#

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.

Parameters:

X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series) – Series or DataFrame of shape (n_samples, n_features). The data matrix for which we want to get the predictions.

Returns:

The predicted values.

Return type:

bigframes.dataframe.DataFrame

score(X: DataFrame | Series | DataFrame | Series, y: DataFrame | Series | DataFrame | Series)[source]#

Calculate evaluation metrics of the model.

Note

Output matches that of the BigQuery ML.EVALUATE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#regression_models for the outputs relevant to this model type.

Parameters:
Returns:

The DataFrame as evaluation result.

Return type:

bigframes.dataframe.DataFrame

to_gbq(model_name: str, replace: bool = False) → RandomForestRegressor[source]#

Save the model to BigQuery.

Parameters:

  • model_name (str) – The name of the model.

  • replace (bool, default False) – Determine whether to replace if the model already exists. Default to False.

Returns:

Saved model.

Return type:

RandomForestRegressor