Inheritance diagram for xgboost.sklearn.XGBClassifier:

Public Member Functions
None	__init__ (self, SklObjective objective="binary:logistic", *Any kwargs)

"XGBClassifier"	fit (self, ArrayLike X, ArrayLike y, *Optional[ArrayLike] sample_weight=None, Optional[ArrayLike] base_margin=None, Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] eval_set=None, Optional[Union[str, Sequence[str], Metric]] eval_metric=None, Optional[int] early_stopping_rounds=None, Optional[Union[bool, int]] verbose=True, Optional[Union[Booster, str, XGBModel]] xgb_model=None, Optional[Sequence[ArrayLike]] sample_weight_eval_set=None, Optional[Sequence[ArrayLike]] base_margin_eval_set=None, Optional[ArrayLike] feature_weights=None, Optional[Sequence[TrainingCallback]] callbacks=None)

ArrayLike	predict (self, ArrayLike X, bool output_margin=False, bool validate_features=True, Optional[ArrayLike] base_margin=None, Optional[Tuple[int, int]] iteration_range=None)

np.ndarray	predict_proba (self, ArrayLike X, bool validate_features=True, Optional[ArrayLike] base_margin=None, Optional[Tuple[int, int]] iteration_range=None)

np.ndarray	classes_ (self)

Public Member Functions inherited from xgboost.sklearn.XGBModel
bool	__sklearn_is_fitted__ (self)

Booster	get_booster (self)

"XGBModel"	set_params (self, **Any params)

Dict[str, Any]	get_params (self, bool deep=True)

Dict[str, Any]	get_xgb_params (self)

int	get_num_boosting_rounds (self)

None	save_model (self, Union[str, os.PathLike] fname)

None	load_model (self, ModelIn fname)

np.ndarray	apply (self, ArrayLike X, Optional[Tuple[int, int]] iteration_range=None)

Dict[str, Dict[str, List[float]]]	evals_result (self)

int	n_features_in_ (self)

np.ndarray	feature_names_in_ (self)

float	best_score (self)

int	best_iteration (self)

np.ndarray	feature_importances_ (self)

np.ndarray	coef_ (self)

np.ndarray	intercept_ (self)

Data Fields
	n_classes_

	objective

Data Fields inherited from xgboost.sklearn.XGBModel
	n_estimators

	objective

	max_depth

	max_leaves

	max_bin

	grow_policy

	learning_rate

	verbosity

	booster

	tree_method

	gamma

	min_child_weight

	max_delta_step

	subsample

	sampling_method

	colsample_bytree

	colsample_bylevel

	colsample_bynode

	reg_alpha

	reg_lambda

	scale_pos_weight

	base_score

	missing

	num_parallel_tree

	random_state

	n_jobs

	monotone_constraints

	interaction_constraints

	importance_type

	device

	validate_parameters

	enable_categorical

	feature_types

	max_cat_to_onehot

	max_cat_threshold

	multi_strategy

	eval_metric

	early_stopping_rounds

	callbacks

	kwargs

	n_classes_

	evals_result_

Static Public Attributes
str	extra_parameters

Protected Attributes
	_Booster

Protected Attributes inherited from xgboost.sklearn.XGBModel
	_Booster

Additional Inherited Members
Protected Member Functions inherited from xgboost.sklearn.XGBModel
Dict[str, bool]	_more_tags (self)

str	_get_type (self)

None	_load_model_attributes (self, dict config)

Tuple[ Optional[Union[Booster, str, "XGBModel"]], Optional[Metric], Dict[str, Any], Optional[int], Optional[Sequence[TrainingCallback]],]	_configure_fit (self, Optional[Union[Booster, "XGBModel", str]] booster, Optional[Union[Callable, str, Sequence[str]]] eval_metric, Dict[str, Any] params, Optional[int] early_stopping_rounds, Optional[Sequence[TrainingCallback]] callbacks)

DMatrix	_create_dmatrix (self, Optional[DMatrix] ref, **Any kwargs)

None	_set_evaluation_result (self, TrainingCallback.EvalsLog evals_result)

bool	_can_use_inplace_predict (self)

Tuple[int, int]	_get_iteration_range (self, Optional[Tuple[int, int]] iteration_range)

Constructor & Destructor Documentation

◆ init()

None xgboost.sklearn.XGBClassifier.__init__	(		self,
		*SklObjective	objective = `"binary:logistic"`,
		**Any	kwargs
	)

Reimplemented from xgboost.sklearn.XGBModel.

Member Function Documentation

◆ fit()

"XGBClassifier" xgboost.sklearn.XGBClassifier.fit	(		self,
		ArrayLike	X,
		ArrayLike	y,
		*Optional[ArrayLike]	sample_weight = `None`,
		Optional[ArrayLike]	base_margin = `None`,
		Optional[Sequence[Tuple[ArrayLike, ArrayLike]]]	eval_set = `None`,
		Optional[Union[str, Sequence[str], Metric]]	eval_metric = `None`,
		Optional[int]	early_stopping_rounds = `None`,
		Optional[Union[bool, int]]	verbose = `True`,
		Optional[Union[Booster, str, XGBModel]]	xgb_model = `None`,
		Optional[Sequence[ArrayLike]]	sample_weight_eval_set = `None`,
		Optional[Sequence[ArrayLike]]	base_margin_eval_set = `None`,
		Optional[ArrayLike]	feature_weights = `None`,
		Optional[Sequence[TrainingCallback]]	callbacks = `None`
	)

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
----------
X :
    Feature matrix. See :ref:`py-data` for a list of supported types.

    When the ``tree_method`` is set to ``hist``, internally, the
    :py:class:`QuantileDMatrix` will be used instead of the :py:class:`DMatrix`
    for conserving memory. However, this has performance implications when the
    device of input data is not matched with algorithm. For instance, if the
    input is a numpy array on CPU but ``cuda`` is used for training, then the
    data is first processed on CPU then transferred to GPU.
y :
    Labels
sample_weight :
    instance weights
base_margin :
    global bias for each instance.
eval_set :
    A list of (X, y) tuple pairs to use as validation sets, for which
    metrics will be computed.
    Validation metrics will help us track the performance of the model.

eval_metric : str, list of str, or callable, optional

    .. deprecated:: 1.6.0

    Use `eval_metric` in :py:meth:`__init__` or :py:meth:`set_params` instead.

early_stopping_rounds : int

    .. deprecated:: 1.6.0

    Use `early_stopping_rounds` in :py:meth:`__init__` or :py:meth:`set_params`
    instead.
verbose :
    If `verbose` is True and an evaluation set is used, the evaluation metric
    measured on the validation set is printed to stdout at each boosting stage.
    If `verbose` is an integer, the evaluation metric is printed at each
    `verbose` boosting stage. The last boosting stage / the boosting stage found
    by using `early_stopping_rounds` is also printed.
xgb_model :
    file name of stored XGBoost model or 'Booster' instance XGBoost model to be
    loaded before training (allows training continuation).
sample_weight_eval_set :
    A list of the form [L_1, L_2, ..., L_n], where each L_i is an array like
    object storing instance weights for the i-th validation set.
base_margin_eval_set :
    A list of the form [M_1, M_2, ..., M_n], where each M_i is an array like
    object storing base margin for the i-th validation set.
feature_weights :
    Weight for each feature, defines the probability of each feature being
    selected when colsample is being used.  All values must be greater than 0,
    otherwise a `ValueError` is thrown.

callbacks :
    .. deprecated:: 1.6.0
        Use `callbacks` in :py:meth:`__init__` or :py:meth:`set_params` instead.

Reimplemented from xgboost.sklearn.XGBModel.

Reimplemented in xgboost.sklearn.XGBRFClassifier.

◆ predict()

ArrayLike xgboost.sklearn.XGBClassifier.predict	(		self,
		ArrayLike	X,
		bool	output_margin = `False`,
		bool	validate_features = `True`,
		Optional[ArrayLike]	base_margin = `None`,
		Optional[Tuple[int, int]]	iteration_range = `None`
	)

Predict with `X`.  If the model is trained with early stopping, then
:py:attr:`best_iteration` is used automatically. The estimator uses
`inplace_predict` by default and falls back to using :py:class:`DMatrix` if
devices between the data and the estimator don't match.

.. note:: This function is only thread safe for `gbtree` and `dart`.

Parameters
----------
X :
    Data to predict with.
output_margin :
    Whether to output the raw untransformed margin value.
validate_features :
    When this is True, validate that the Booster's and data's feature_names are
    identical.  Otherwise, it is assumed that the feature_names are the same.
base_margin :
    Margin added to prediction.
iteration_range :
    Specifies which layer of trees are used in prediction.  For example, if a
    random forest is trained with 100 rounds.  Specifying ``iteration_range=(10,
    20)``, then only the forests built during [10, 20) (half open set) rounds
    are used in this prediction.

    .. versionadded:: 1.4.0

Returns
-------
prediction

Reimplemented from xgboost.sklearn.XGBModel.

◆ predict_proba()

np.ndarray xgboost.sklearn.XGBClassifier.predict_proba	(		self,
		ArrayLike	X,
		bool	validate_features = `True`,
		Optional[ArrayLike]	base_margin = `None`,
		Optional[Tuple[int, int]]	iteration_range = `None`
	)

Predict the probability of each `X` example being of a given class. If the
model is trained with early stopping, then :py:attr:`best_iteration` is used
automatically. The estimator uses `inplace_predict` by default and falls back to
using :py:class:`DMatrix` if devices between the data and the estimator don't
match.

.. note:: This function is only thread safe for `gbtree` and `dart`.

Parameters
----------
X :
    Feature matrix. See :ref:`py-data` for a list of supported types.
validate_features :
    When this is True, validate that the Booster's and data's feature_names are
    identical.  Otherwise, it is assumed that the feature_names are the same.
base_margin :
    Margin added to prediction.
iteration_range :
    Specifies which layer of trees are used in prediction.  For example, if a
    random forest is trained with 100 rounds.  Specifying `iteration_range=(10,
    20)`, then only the forests built during [10, 20) (half open set) rounds are
    used in this prediction.

Returns
-------
prediction :
    a numpy array of shape array-like of shape (n_samples, n_classes) with the
    probability of each data example being of a given class.

Field Documentation

◆ extra_parameters

str xgboost.sklearn.XGBClassifier.extra_parameters

static

Initial value:

= """
    n_estimators : Optional[int]
        Number of trees in random forest to fit.
""",

The documentation for this class was generated from the following file:

External/xgboost/python-package/xgboost/sklearn.py

Public Member Functions

Data Fields

Static Public Attributes

Protected Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ fit()

◆ predict()

◆ predict_proba()

Field Documentation

◆ extra_parameters

◆ init()