bigframes.ml.ensemble.RandomForestClassifier#

class bigframes.ml.ensemble.RandomForestClassifier(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 classifier.

A random forest is a meta estimator that fits a number of decision tree classifiers 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.
subsample (Optional[float]) 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 (xgbs alpha). Default to 0.0.
reg_lambda (Optional[float]) L2 regularization term on weights (xgbs 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 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

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#classification_models for the outputs relevant to this model type.

Parameters:

X (bigframes.dataframe.DataFrame or bigframes.series.Series) A BigQuery DataFrame as evaluation data.
y (bigframes.dataframe.DataFrame or bigframes.series.Series) A BigQuery DataFrame as evaluation labels.

Returns:

The DataFrame as evaluation result.

Return type:

bigframes.dataframe.DataFrame

to_gbq(model_name: str, replace: bool = False) → RandomForestClassifier[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:

RandomForestClassifier

bigframes.ml.ensemble

bigframes.ml.ensemble.RandomForestRegressor