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: from abc import ABCMeta
  from abc import abstractmethod
  from sklearn.base import MultiOutputMixin
  from sklearn.base import BaseEstimator
  from sklearn.base import is_classifier, clone
  from sklearn.utils import Bunch
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from scipy.sparse import issparse
  from ._tree import Tree
  from ._tree import _build_pruned_tree_ccp
  from ._tree import ccp_pruning_path
  from .None import _tree, _splitter
label_desc: Use image node_id 1 to create a new BaseDecisionTree object from inherited base classes: MultiOutputMixin, BaseEstimator with example: obj = BaseDecisionTree()