class_docstr: Abstract base class for Gradient Boosting.
class_docstr_tok: ["Abstract","base","class","for","Gradient","Boosting","."]
code: def __init__(
      self,
      *,
      loss,
      learning_rate,
      n_estimators,
      criterion,
      min_samples_split,
      min_samples_leaf,
      min_weight_fraction_leaf,
      max_depth,
      min_impurity_decrease,
      init,
      subsample,
      max_features,
      ccp_alpha,
      random_state,
      alpha=0.9,
      verbose=0,
      max_leaf_nodes=None,
      warm_start=False,
      validation_fraction=0.1,
      n_iter_no_change=None,
      tol=1e-4,
  ):
      self.n_estimators = n_estimators
      self.learning_rate = learning_rate
      self.loss = loss
      self.criterion = criterion
      self.min_samples_split = min_samples_split
      self.min_samples_leaf = min_samples_leaf
      self.min_weight_fraction_leaf = min_weight_fraction_leaf
      self.subsample = subsample
      self.max_features = max_features
      self.max_depth = max_depth
      self.min_impurity_decrease = min_impurity_decrease
      self.ccp_alpha = ccp_alpha
      self.init = init
      self.random_state = random_state
      self.alpha = alpha
      self.verbose = verbose
      self.max_leaf_nodes = max_leaf_nodes
      self.warm_start = warm_start
      self.validation_fraction = validation_fraction
      self.n_iter_no_change = n_iter_no_change
      self.tol = tol
  
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 1 to create a new BaseGradientBoosting object from inherited base classes: BaseEnsemble with example: obj = BaseGradientBoosting()