bigframes.ml.ensemble.XGBClassifier#

class bigframes.ml.ensemble.XGBClassifier(n_estimators: int = 1, *, booster: Literal['gbtree', 'dart'] = 'gbtree', dart_normalized_type: Literal['tree', 'forest'] = 'tree', 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 = 1.0, gamma: float = 0.0, max_depth: int = 6, subsample: float = 1.0, reg_alpha: float = 0.0, reg_lambda: float = 1.0, learning_rate: float = 0.3, max_iterations: int = 20, tol: float = 0.01, enable_global_explain: bool = False, xgboost_version: Literal['0.9', '1.1'] = '0.9')[source]#

XGBoost classifier model.

Parameters:

• n_estimators (Optional[int]) Number of parallel trees constructed during each iteration. Default to 1.

• booster (Optional[str]) Specify which booster to use: gbtree or dart. Default to gbtree.

• dart_normalized_type (Optional[str]) Type of normalization algorithm for DART booster. Possible values: TREE, FOREST. Default to TREE.

• 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.

• colsample_bylevel (Optional[float]) Subsample ratio of columns for each level. Default to 1.0.

• colsample_bynode (Optional[float]) Subsample ratio of columns for each split. Default to 1.0.

• 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 6.

• subsample (Optional[float]) Subsample ratio of the training instance. Default to 1.0.

• 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.

• learning_rate (Optional[float]) Boosting learning rate (xgbs eta). Default to 0.3.

• max_iterations (Optional[int]) Maximum number of rounds for boosting. Default to 20.

• 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 using the XGB model.

Parameters:

X (bigframes.dataframe.DataFrame or bigframes.series.Series) Series or DataFrame of shape (n_samples, n_features). Samples.

Returns:

DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.

Return type:

bigframes.dataframe.DataFrame

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

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy, which is a harsh metric since you require that each label set be correctly predicted for each sample.

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) DataFrame of shape (n_samples, n_features). Test samples.

• y (bigframes.dataframe.DataFrame or bigframes.series.Series) DataFrame of shape (n_samples,) or (n_samples, n_outputs). True labels for X.

Returns:

A DataFrame of the evaluation result.

Return type:

bigframes.dataframe.DataFrame

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

XGBClassifier