class_docstr: Base class for histogram-based gradient boosting estimators.
class_docstr_tok: ["Base","class","for","histogram-based","gradient","boosting","estimators","."]
code: def __init__(
      self,
      loss,
      *,
      learning_rate,
      max_iter,
      max_leaf_nodes,
      max_depth,
      min_samples_leaf,
      l2_regularization,
      max_bins,
      categorical_features,
      monotonic_cst,
      interaction_cst,
      warm_start,
      early_stopping,
      scoring,
      validation_fraction,
      n_iter_no_change,
      tol,
      verbose,
      random_state,
  ):
      self.loss = loss
      self.learning_rate = learning_rate
      self.max_iter = max_iter
      self.max_leaf_nodes = max_leaf_nodes
      self.max_depth = max_depth
      self.min_samples_leaf = min_samples_leaf
      self.l2_regularization = l2_regularization
      self.max_bins = max_bins
      self.monotonic_cst = monotonic_cst
      self.interaction_cst = interaction_cst
      self.categorical_features = categorical_features
      self.warm_start = warm_start
      self.early_stopping = early_stopping
      self.scoring = scoring
      self.validation_fraction = validation_fraction
      self.n_iter_no_change = n_iter_no_change
      self.tol = tol
      self.verbose = verbose
      self.random_state = random_state
  
imports: import itertools
  from abc import ABC, abstractmethod
  from functools import partial
  from numbers import Integral, Real
  from timeit import default_timer
  import numpy
  from ..._loss.loss import _LOSSES, BaseLoss, HalfBinomialLoss, HalfGammaLoss, HalfMultinomialLoss, HalfPoissonLoss, PinballLoss
  from ...base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ...metrics import check_scoring
  from ...model_selection import train_test_split
  from ...preprocessing import LabelEncoder
  from ...utils import check_random_state, compute_sample_weight, resample
  from ...utils._openmp_helpers import _openmp_effective_n_threads
  from ...utils._param_validation import Interval, RealNotInt, StrOptions
  from ...utils.multiclass import check_classification_targets
  from ...utils.validation import _check_monotonic_cst, _check_sample_weight, check_consistent_length, check_is_fitted
  from ._gradient_boosting import _update_raw_predictions
  from .binning import _BinMapper
  from .common import G_H_DTYPE, X_DTYPE, Y_DTYPE
  from .grower import TreeGrower
label_desc: Use image node_id 1 to create a new BaseHistGradientBoosting object from inherited base classes: BaseEstimator, ABC with example: obj = BaseHistGradientBoosting(loss)