class_docstr: Gradient Boosting for classification.
  
  This algorithm builds an additive model in a forward stage-wise fashion; it
  allows for the optimization of arbitrary differentiable loss functions. In
  each stage ``n_classes_`` regression trees are fit on the negative gradient
  of the loss function, e.g. binary or multiclass log loss. Binary
  classification is a special case where only a single regression tree is
  induced.
  
  :class:`sklearn.ensemble.HistGradientBoostingClassifier` is a much faster
  variant of this algorithm for intermediate datasets (`n_samples >= 10_000`).
  
  Read more in the :ref:`User Guide <gradient_boosting>`.
  
  Parameters
  ----------
  loss : {'log_loss', 'exponential'}, default='log_loss'
      The loss function to be optimized. 'log_loss' refers to binomial and
      multinomial deviance, the same as used in logistic regression.
      It is a good choice for classification with probabilistic outputs.
      For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.
  
  learning_rate : float, default=0.1
      Learning rate shrinks the contribution of each tree by `learning_rate`.
      There is a trade-off between learning_rate and n_estimators.
      Values must be in the range `[0.0, inf)`.
  
  n_estimators : int, default=100
      The number of boosting stages to perform. Gradient boosting
      is fairly robust to over-fitting so a large number usually
      results in better performance.
      Values must be in the range `[1, inf)`.
  
  subsample : float, default=1.0
      The fraction of samples to be used for fitting the individual base
      learners. If smaller than 1.0 this results in Stochastic Gradient
      Boosting. `subsample` interacts with the parameter `n_estimators`.
      Choosing `subsample < 1.0` leads to a reduction of variance
      and an increase in bias.
      Values must be in the range `(0.0, 1.0]`.
  
  criterion : {'friedman_mse', 'squared_error'}, default='friedman_mse'
      The function to measure the quality of a split. Supported criteria are
      'friedman_mse' for the mean squared error with improvement score by
      Friedman, 'squared_error' for mean squared error. The default value of
      'friedman_mse' is generally the best as it can provide a better
      approximation in some cases.
  
      .. versionadded:: 0.18
  
  min_samples_split : int or float, default=2
      The minimum number of samples required to split an internal node:
  
      - If int, values must be in the range `[2, inf)`.
      - If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`
        will be `ceil(min_samples_split * n_samples)`.
  
      .. versionchanged:: 0.18
         Added float values for fractions.
  
  min_samples_leaf : int or float, default=1
      The minimum number of samples required to be at a leaf node.
      A split point at any depth will only be considered if it leaves at
      least ``min_samples_leaf`` training samples in each of the left and
      right branches.  This may have the effect of smoothing the model,
      especially in regression.
  
      - If int, values must be in the range `[1, inf)`.
      - If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`
        will be `ceil(min_samples_leaf * n_samples)`.
  
      .. versionchanged:: 0.18
         Added float values for fractions.
  
  min_weight_fraction_leaf : float, default=0.0
      The minimum weighted fraction of the sum total of weights (of all
      the input samples) required to be at a leaf node. Samples have
      equal weight when sample_weight is not provided.
      Values must be in the range `[0.0, 0.5]`.
  
  max_depth : int or None, default=3
      Maximum depth of the individual regression estimators. The maximum
      depth limits the number of nodes in the tree. Tune this parameter
      for best performance; the best value depends on the interaction
      of the input variables. If None, then nodes are expanded until
      all leaves are pure or until all leaves contain less than
      min_samples_split samples.
      If int, values must be in the range `[1, inf)`.
  
  min_impurity_decrease : float, default=0.0
      A node will be split if this split induces a decrease of the impurity
      greater than or equal to this value.
      Values must be in the range `[0.0, inf)`.
  
      The weighted impurity decrease equation is the following::
  
          N_t / N * (impurity - N_t_R / N_t * right_impurity
                              - N_t_L / N_t * left_impurity)
  
      where ``N`` is the total number of samples, ``N_t`` is the number of
      samples at the current node, ``N_t_L`` is the number of samples in the
      left child, and ``N_t_R`` is the number of samples in the right child.
  
      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
      if ``sample_weight`` is passed.
  
      .. versionadded:: 0.19
  
  init : estimator or 'zero', default=None
      An estimator object that is used to compute the initial predictions.
      ``init`` has to provide :term:`fit` and :term:`predict_proba`. If
      'zero', the initial raw predictions are set to zero. By default, a
      ``DummyEstimator`` predicting the classes priors is used.
  
  random_state : int, RandomState instance or None, default=None
      Controls the random seed given to each Tree estimator at each
      boosting iteration.
      In addition, it controls the random permutation of the features at
      each split (see Notes for more details).
      It also controls the random splitting of the training data to obtain a
      validation set if `n_iter_no_change` is not None.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  max_features : {'sqrt', 'log2'}, int or float, default=None
      The number of features to consider when looking for the best split:
  
      - If int, values must be in the range `[1, inf)`.
      - If float, values must be in the range `(0.0, 1.0]` and the features
        considered at each split will be `max(1, int(max_features * n_features_in_))`.
      - If 'sqrt', then `max_features=sqrt(n_features)`.
      - If 'log2', then `max_features=log2(n_features)`.
      - If None, then `max_features=n_features`.
  
      Choosing `max_features < n_features` leads to a reduction of variance
      and an increase in bias.
  
      Note: the search for a split does not stop until at least one
      valid partition of the node samples is found, even if it requires to
      effectively inspect more than ``max_features`` features.
  
  verbose : int, default=0
      Enable verbose output. If 1 then it prints progress and performance
      once in a while (the more trees the lower the frequency). If greater
      than 1 then it prints progress and performance for every tree.
      Values must be in the range `[0, inf)`.
  
  max_leaf_nodes : int, default=None
      Grow trees with ``max_leaf_nodes`` in best-first fashion.
      Best nodes are defined as relative reduction in impurity.
      Values must be in the range `[2, inf)`.
      If `None`, then unlimited number of leaf nodes.
  
  warm_start : bool, default=False
      When set to ``True``, reuse the solution of the previous call to fit
      and add more estimators to the ensemble, otherwise, just erase the
      previous solution. See :term:`the Glossary <warm_start>`.
  
  validation_fraction : float, default=0.1
      The proportion of training data to set aside as validation set for
      early stopping. Values must be in the range `(0.0, 1.0)`.
      Only used if ``n_iter_no_change`` is set to an integer.
  
      .. versionadded:: 0.20
  
  n_iter_no_change : int, default=None
      ``n_iter_no_change`` is used to decide if early stopping will be used
      to terminate training when validation score is not improving. By
      default it is set to None to disable early stopping. If set to a
      number, it will set aside ``validation_fraction`` size of the training
      data as validation and terminate training when validation score is not
      improving in all of the previous ``n_iter_no_change`` numbers of
      iterations. The split is stratified.
      Values must be in the range `[1, inf)`.
  
      .. versionadded:: 0.20
  
  tol : float, default=1e-4
      Tolerance for the early stopping. When the loss is not improving
      by at least tol for ``n_iter_no_change`` iterations (if set to a
      number), the training stops.
      Values must be in the range `[0.0, inf)`.
  
      .. versionadded:: 0.20
  
  ccp_alpha : non-negative float, default=0.0
      Complexity parameter used for Minimal Cost-Complexity Pruning. The
      subtree with the largest cost complexity that is smaller than
      ``ccp_alpha`` will be chosen. By default, no pruning is performed.
      Values must be in the range `[0.0, inf)`.
      See :ref:`minimal_cost_complexity_pruning` for details.
  
      .. versionadded:: 0.22
  
  Attributes
  ----------
  n_estimators_ : int
      The number of estimators as selected by early stopping (if
      ``n_iter_no_change`` is specified). Otherwise it is set to
      ``n_estimators``.
  
      .. versionadded:: 0.20
  
  feature_importances_ : ndarray of shape (n_features,)
      The impurity-based feature importances.
      The higher, the more important the feature.
      The importance of a feature is computed as the (normalized)
      total reduction of the criterion brought by that feature.  It is also
      known as the Gini importance.
  
      Warning: impurity-based feature importances can be misleading for
      high cardinality features (many unique values). See
      :func:`sklearn.inspection.permutation_importance` as an alternative.
  
  oob_improvement_ : ndarray of shape (n_estimators,)
      The improvement in loss on the out-of-bag samples
      relative to the previous iteration.
      ``oob_improvement_[0]`` is the improvement in
      loss of the first stage over the ``init`` estimator.
      Only available if ``subsample < 1.0``.
  
  oob_scores_ : ndarray of shape (n_estimators,)
      The full history of the loss values on the out-of-bag
      samples. Only available if `subsample < 1.0`.
  
      .. versionadded:: 1.3
  
  oob_score_ : float
      The last value of the loss on the out-of-bag samples. It is
      the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.
  
      .. versionadded:: 1.3
  
  train_score_ : ndarray of shape (n_estimators,)
      The i-th score ``train_score_[i]`` is the loss of the
      model at iteration ``i`` on the in-bag sample.
      If ``subsample == 1`` this is the loss on the training data.
  
  init_ : estimator
      The estimator that provides the initial predictions.
      Set via the ``init`` argument or ``loss.init_estimator``.
  
  estimators_ : ndarray of DecisionTreeRegressor of             shape (n_estimators, ``loss_.K``)
      The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary
      classification, otherwise n_classes.
  
  classes_ : ndarray of shape (n_classes,)
      The classes labels.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_classes_ : int
      The number of classes.
  
  max_features_ : int
      The inferred value of max_features.
  
  See Also
  --------
  HistGradientBoostingClassifier : Histogram-based Gradient Boosting
      Classification Tree.
  sklearn.tree.DecisionTreeClassifier : A decision tree classifier.
  RandomForestClassifier : A meta-estimator that fits a number of decision
      tree classifiers on various sub-samples of the dataset and uses
      averaging to improve the predictive accuracy and control over-fitting.
  AdaBoostClassifier : A meta-estimator that begins by fitting a classifier
      on the original dataset and then fits additional copies of the
      classifier on the same dataset where the weights of incorrectly
      classified instances are adjusted such that subsequent classifiers
      focus more on difficult cases.
  
  Notes
  -----
  The features are always randomly permuted at each split. Therefore,
  the best found split may vary, even with the same training data and
  ``max_features=n_features``, if the improvement of the criterion is
  identical for several splits enumerated during the search of the best
  split. To obtain a deterministic behaviour during fitting,
  ``random_state`` has to be fixed.
  
  References
  ----------
  J. Friedman, Greedy Function Approximation: A Gradient Boosting
  Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.
  
  J. Friedman, Stochastic Gradient Boosting, 1999
  
  T. Hastie, R. Tibshirani and J. Friedman.
  Elements of Statistical Learning Ed. 2, Springer, 2009.
  
  Examples
  --------
  The following example shows how to fit a gradient boosting classifier with
  100 decision stumps as weak learners.
  
  >>> from sklearn.datasets import make_hastie_10_2
  >>> from sklearn.ensemble import GradientBoostingClassifier
  
  >>> X, y = make_hastie_10_2(random_state=0)
  >>> X_train, X_test = X[:2000], X[2000:]
  >>> y_train, y_test = y[:2000], y[2000:]
  
  >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
  ...     max_depth=1, random_state=0).fit(X_train, y_train)
  >>> clf.score(X_test, y_test)
  0.913...
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docstr: Predict class probabilities at each stage for X.
  
  This method allows monitoring (i.e. determine error on testing set)
  after each stage.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csr_matrix``.
  
  Yields
  ------
  y : generator of ndarray of shape (n_samples,)
      The predicted value of the input samples.
code: def staged_predict_proba(self, X):
      """Predict class probabilities at each stage for X.
  
      This method allows monitoring (i.e. determine error on testing set)
      after each stage.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csr_matrix``.
  
      Yields
      ------
      y : generator of ndarray of shape (n_samples,)
          The predicted value of the input samples.
      """
      try:
          for raw_predictions in self._staged_raw_predict(X):
              yield self._loss._raw_prediction_to_proba(raw_predictions)
      except NotFittedError:
          raise
      except AttributeError as e:
          raise AttributeError(
              "loss=%r does not support predict_proba" % self.loss
          ) from e
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  from time import time
  import numpy
  from scipy.sparse import csc_matrix, csr_matrix, issparse
  from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import NotFittedError
  from ..model_selection import train_test_split
  from ..tree import DecisionTreeRegressor
  from ..tree._tree import DOUBLE, DTYPE
  from ..utils import check_array, check_random_state, column_or_1d
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _check_sample_weight, check_is_fitted
  from .None import _gb_losses
  from ._base import BaseEnsemble
  from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages
label_desc: Use image node_id 9 for calling the GradientBoostingClassifier obj's underlying member method code with example usage: obj.staged_predict_proba(X) without return types