Class XGBClassifier (1.19.0)

Version 1.19.0

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XGBClassifier(
    n_estimators: int = 1,
    *,
    booster: typing.Literal["gbtree", "dart"] = "gbtree",
    dart_normalized_type: typing.Literal["tree", "forest"] = "tree",
    tree_method: typing.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: typing.Literal["0.9", "1.1"] = "0.9"
)

XGBoost classifier model.

Parameters
Name	Description
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 (xgb's alpha). Default to 0.0.
reg_lambda	Optional[float] L2 regularization term on weights (xgb's lambda). Default to 1.0.
learning_rate	Optional[float] Boosting learning rate (xgb's "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".

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Fit gradient boosting model.

Note that calling fit() multiple times will cause the model object to be re-fit from scratch. To resume training from a previous checkpoint, explicitly pass xgb_model argument.

Parameters
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series Series or DataFrame of shape (n_samples, n_features). Training data.
y	bigframes.dataframe.DataFrame or bigframes.series.Series DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary.

Returns
Type	Description
XGBModel	Fitted estimator.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
deep	bool, default True Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
Dictionary	A dictionary of parameter names mapped to their values.

predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict using the XGB model.

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

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.

register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

After register, go to the Google Cloud console (https://console.cloud.google.com/vertex-ai/models) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.

Parameter
Name	Description
vertex_ai_model_id	Optional[str], default None Optional string id as model id in Vertex. If not set, will default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.

score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
)

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
Name	Description
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
Type	Description
bigframes.dataframe.DataFrame	A DataFrame of the evaluation result.

to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.ensemble.XGBClassifier

Save the model to BigQuery.

Parameters
Name	Description
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
Type	Description
XGBClassifier	Saved model.