xgboost.sklearn.XGBModel Class Reference

Inheritance diagram for xgboost.sklearn.XGBModel:

Public Member Functions
None	__init__ (self, Optional[int] max_depth=None, Optional[int] max_leaves=None, Optional[int] max_bin=None, Optional[str] grow_policy=None, Optional[float] learning_rate=None, Optional[int] n_estimators=None, Optional[int] verbosity=None, SklObjective objective=None, Optional[str] booster=None, Optional[str] tree_method=None, Optional[int] n_jobs=None, Optional[float] gamma=None, Optional[float] min_child_weight=None, Optional[float] max_delta_step=None, Optional[float] subsample=None, Optional[str] sampling_method=None, Optional[float] colsample_bytree=None, Optional[float] colsample_bylevel=None, Optional[float] colsample_bynode=None, Optional[float] reg_alpha=None, Optional[float] reg_lambda=None, Optional[float] scale_pos_weight=None, Optional[float] base_score=None, Optional[Union[np.random.RandomState, int]] random_state=None, float missing=np.nan, Optional[int] num_parallel_tree=None, Optional[Union[Dict[str, int], str]] monotone_constraints=None, Optional[Union[str, Sequence[Sequence[str]]]] interaction_constraints=None, Optional[str] importance_type=None, Optional[str] device=None, Optional[bool] validate_parameters=None, bool enable_categorical=False, Optional[FeatureTypes] feature_types=None, Optional[int] max_cat_to_onehot=None, Optional[int] max_cat_threshold=None, Optional[str] multi_strategy=None, Optional[Union[str, List[str], Callable]] eval_metric=None, Optional[int] early_stopping_rounds=None, Optional[List[TrainingCallback]] callbacks=None, **Any kwargs)
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)
"XGBModel"	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	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_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_

Protected Member Functions
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)

Protected Attributes
	_Booster

Member Function Documentation

_configure_fit()

Tuple[ Optional[Union[Booster, str, "XGBModel"]], Optional[Metric], Dict[str, Any], Optional[int], Optional[Sequence[TrainingCallback]], ] xgboost.sklearn.XGBModel._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  )

protected

Configure parameters for :py:meth:`fit`.

_load_model_attributes()

None xgboost.sklearn.XGBModel._load_model_attributes (   self, dict  config  )

protected

Load model attributes without hyper-parameters.

_more_tags()

Dict[str, bool] xgboost.sklearn.XGBModel._more_tags (   self )

protected

Tags used for scikit-learn data validation.

apply()

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

Return the predicted leaf every tree for each sample. If the model is trained
with early stopping, then :py:attr:`best_iteration` is used automatically.

Parameters
----------
X : array_like, shape=[n_samples, n_features]
    Input features matrix.

iteration_range :
    See :py:meth:`predict`.

Returns
-------
X_leaves : array_like, shape=[n_samples, n_trees]
    For each datapoint x in X and for each tree, return the index of the
    leaf x ends up in. Leaves are numbered within
    ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering.

Reimplemented in xgboost.dask.DaskScikitLearnBase, and xgboost.sklearn.XGBRanker.

best_iteration()

int xgboost.sklearn.XGBModel.best_iteration

(

self

)

The best iteration obtained by early stopping.  This attribute is 0-based,
for instance if the best iteration is the first round, then best_iteration is 0.

best_score()

float xgboost.sklearn.XGBModel.best_score

(

self

)

The best score obtained by early stopping.

coef_()

np.ndarray xgboost.sklearn.XGBModel.coef_

(

self

)

Coefficients property

.. note:: Coefficients are defined only for linear learners

    Coefficients are only defined when the linear model is chosen as
    base learner (`booster=gblinear`). It is not defined for other base
    learner types, such as tree learners (`booster=gbtree`).

Returns
-------
coef_ : array of shape ``[n_features]`` or ``[n_classes, n_features]``

evals_result()

Dict[str, Dict[str, List[float]]] xgboost.sklearn.XGBModel.evals_result

(

self

)

Return the evaluation results.

If **eval_set** is passed to the :py:meth:`fit` function, you can call
``evals_result()`` to get evaluation results for all passed **eval_sets**.  When
**eval_metric** is also passed to the :py:meth:`fit` function, the
**evals_result** will contain the **eval_metrics** passed to the :py:meth:`fit`
function.

The returned evaluation result is a dictionary:

.. code-block:: python

    {'validation_0': {'logloss': ['0.604835', '0.531479']},
     'validation_1': {'logloss': ['0.41965', '0.17686']}}

Returns
-------
evals_result

feature_importances_()

np.ndarray xgboost.sklearn.XGBModel.feature_importances_

(

self

)

Feature importances property, return depends on `importance_type`
parameter. When model trained with multi-class/multi-label/multi-target dataset,
the feature importance is "averaged" over all targets. The "average" is defined
based on the importance type. For instance, if the importance type is
"total_gain", then the score is sum of loss change for each split from all
trees.

Returns
-------
feature_importances_ : array of shape ``[n_features]`` except for multi-class
linear model, which returns an array with shape `(n_features, n_classes)`

feature_names_in_()

np.ndarray xgboost.sklearn.XGBModel.feature_names_in_

(

self

)

Names of features seen during :py:meth:`fit`.  Defined only when `X` has
feature names that are all strings.

fit()

"XGBModel" xgboost.sklearn.XGBModel.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 in xgboost.dask.DaskXGBRanker, xgboost.dask.DaskXGBRegressor, xgboost.dask.DaskXGBClassifier, xgboost.dask.DaskXGBRFRegressor, xgboost.dask.DaskXGBRFClassifier, xgboost.sklearn.XGBRanker, xgboost.sklearn.XGBClassifier, xgboost.sklearn.XGBRFClassifier, and xgboost.sklearn.XGBRFRegressor.

get_booster()

Booster xgboost.sklearn.XGBModel.get_booster

(

self

)

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns
-------
booster : a xgboost booster of underlying model

get_num_boosting_rounds()

int xgboost.sklearn.XGBModel.get_num_boosting_rounds

(

self

)

Gets the number of xgboost boosting rounds.

Reimplemented in xgboost.dask.DaskXGBRFRegressor, xgboost.dask.DaskXGBRFClassifier, xgboost.sklearn.XGBRFClassifier, and xgboost.sklearn.XGBRFRegressor.

get_params()

Dict[str, Any] xgboost.sklearn.XGBModel.get_params	(		self,
		bool	deep = True
	)

Get parameters.

get_xgb_params()

Dict[str, Any] xgboost.sklearn.XGBModel.get_xgb_params

(

self

)

Get xgboost specific parameters.

Reimplemented in xgboost.dask.DaskXGBRFRegressor, xgboost.dask.DaskXGBRFClassifier, xgboost.sklearn.XGBRFClassifier, and xgboost.sklearn.XGBRFRegressor.

intercept_()

np.ndarray xgboost.sklearn.XGBModel.intercept_

(

self

)

Intercept (bias) property

.. note:: Intercept is defined only for linear learners

    Intercept (bias) is only defined when the linear model is chosen as base
    learner (`booster=gblinear`). It is not defined for other base learner types,
    such as tree learners (`booster=gbtree`).

Returns
-------
intercept_ : array of shape ``(1,)`` or ``[n_classes]``

n_features_in_()

int xgboost.sklearn.XGBModel.n_features_in_

(

self

)

Number of features seen during :py:meth:`fit`.

predict()

ArrayLike xgboost.sklearn.XGBModel.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 in xgboost.dask.DaskScikitLearnBase, xgboost.sklearn.XGBClassifier, and xgboost.sklearn.XGBRanker.

set_params()

"XGBModel" xgboost.sklearn.XGBModel.set_params	(		self,
		**Any	params
	)

Set the parameters of this estimator.  Modification of the sklearn method to
allow unknown kwargs. This allows using the full range of xgboost
parameters that are not defined as member variables in sklearn grid
search.

Returns
-------
self

---

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

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