Class XGBRegressor (1.25.0) 

XGBRegressor(
    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 regression 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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    X_eval: typing.Optional[
        typing.Union[
            bigframes.dataframe.DataFrame,
            bigframes.series.Series,
            pandas.core.frame.DataFrame,
            pandas.core.series.Series,
        ]
    ] = None,
    y_eval: typing.Optional[
        typing.Union[
            bigframes.dataframe.DataFrame,
            bigframes.series.Series,
            pandas.core.frame.DataFrame,
            pandas.core.series.Series,
        ]
    ] = None,
) -> 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.
X_eval bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples, n_features). Evaluation data.
y_eval bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples,) or (n_samples, n_targets). Evaluation target values. Will be cast to X_eval'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,
        pandas.core.frame.DataFrame,
        pandas.core.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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
)

Calculate evaluation metrics of the model.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples, n_features). Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples,) or (n_samples, n_outputs). True values 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.XGBRegressor

Save the model to BigQuery.

Parameters
Name Description
model_name str

The name of the model.
replace bool, default False Returns: Saved model.

Determine whether to replace if the model already exists. Default to False.