class_docstr: Base class for decision trees.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
class_docstr_tok: ["Base","class","for","decision","trees",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
code: def __init__(
      self,
      *,
      criterion,
      splitter,
      max_depth,
      min_samples_split,
      min_samples_leaf,
      min_weight_fraction_leaf,
      max_features,
      max_leaf_nodes,
      random_state,
      min_impurity_decrease,
      class_weight=None,
      ccp_alpha=0.0,
  ):
      self.criterion = criterion
      self.splitter = splitter
      self.max_depth = max_depth
      self.min_samples_split = min_samples_split
      self.min_samples_leaf = min_samples_leaf
      self.min_weight_fraction_leaf = min_weight_fraction_leaf
      self.max_features = max_features
      self.max_leaf_nodes = max_leaf_nodes
      self.random_state = random_state
      self.min_impurity_decrease = min_impurity_decrease
      self.class_weight = class_weight
      self.ccp_alpha = ccp_alpha
  
imports: import copy
  import numbers
  import warnings
  from abc import ABCMeta, abstractmethod
  from math import ceil
  from numbers import Integral, Real
  import numpy
  from scipy.sparse import issparse
  from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context, clone, is_classifier
  from ..utils import Bunch, check_random_state, compute_sample_weight
  from ..utils._param_validation import Hidden, Interval, RealNotInt, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _assert_all_finite_element_wise, _check_sample_weight, assert_all_finite, check_is_fitted
  from .None import _criterion, _splitter, _tree
  from ._criterion import Criterion
  from ._splitter import Splitter
  from ._tree import BestFirstTreeBuilder, DepthFirstTreeBuilder, Tree, _build_pruned_tree_ccp, ccp_pruning_path
  from ._utils import _any_isnan_axis0
label_desc: Use image node_id 1 to create a new BaseDecisionTree object from inherited base classes: MultiOutputMixin, BaseEstimator with example: obj = BaseDecisionTree()