class_docstr: Abstract base class for Gradient Boosting.
class_docstr_tok: ["Abstract","base","class","for","Gradient","Boosting","."]
docstr: Compute raw predictions of ``X`` for each iteration.
  
  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``.
  
  check_input : bool, default=True
      If False, the input arrays X will not be checked.
  
  Returns
  -------
  raw_predictions : generator of ndarray of shape (n_samples, k)
      The raw predictions of the input samples. The order of the
      classes corresponds to that in the attribute :term:`classes_`.
      Regression and binary classification are special cases with
      ``k == 1``, otherwise ``k==n_classes``.
code: def _staged_raw_predict(self, X, check_input=True):
      """Compute raw predictions of ``X`` for each iteration.
  
      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``.
  
      check_input : bool, default=True
          If False, the input arrays X will not be checked.
  
      Returns
      -------
      raw_predictions : generator of ndarray of shape (n_samples, k)
          The raw predictions of the input samples. The order of the
          classes corresponds to that in the attribute :term:`classes_`.
          Regression and binary classification are special cases with
          ``k == 1``, otherwise ``k==n_classes``.
      """
      if check_input:
          X = self._validate_data(
              X,
              dtype=DTYPE,
              order="C",
              accept_sparse="csr",
              reset=False,
          )
      raw_predictions = self._raw_predict_init(X)
      for i in range(self.estimators_.shape[0]):
          predict_stage(
              self.estimators_,
              i,
              X,
              self.learning_rate,
              raw_predictions,
          )
          yield raw_predictions.copy()
  
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 15 for calling the BaseGradientBoosting obj's underlying member method code with example usage: obj._staged_raw_predict(X, check_input) without return types