Class XGBRegressor (0.3.0)

XGBRegressor(
    num_parallel_tree: 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=1.0,
    colsample_bylevel=1.0,
    colsample_bynode=1.0,
    gamma=0.0,
    max_depth: int = 6,
    subsample=1.0,
    reg_alpha=0.0,
    reg_lambda=1.0,
    early_stop=True,
    learning_rate=0.3,
    max_iterations: int = 20,
    min_rel_progress=0.01,
    enable_global_explain=False,
    xgboost_version: typing.Literal["0.9", "1.1"] = "0.9",
)

XGBoost regression model.

Parameters
Name	Description
`num_parallel_tree`	`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.
`early_stop`	`Optional[bool]` Whether training should stop after the first iteration. Default to True.
`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.
`min_rel_progress`	`Optional[float]` Minimum relative loss improvement necessary to continue training when early_stop is set to True. 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],
    transforms: typing.Optional[typing.List[str]] = None,
) -> bigframes.ml.ensemble.XGBRegressor

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.
`transforms`	`Optional[List[str]], default None` Do not use. Internal param to be deprecated. Use bigframes.ml.pipeline instead.

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
`DataFrame of shape (n_samples,)`	Returns predicted values.

register

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

After register, go to 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 by 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 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.` whether to replace if the model already exists. Default to False.