Class XGBClassifier (1.31.0) 

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,
        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