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_split,
      class_weight=None,
      presort=False,
  ):
      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.random_state = random_state
      self.max_leaf_nodes = max_leaf_nodes
      self.min_impurity_split = min_impurity_split
      self.class_weight = class_weight
      self.presort = presort
  
      self.n_features_ = None
      self.n_outputs_ = None
      self.classes_ = None
      self.n_classes_ = None
  
      self.tree_ = None
      self.max_features_ = None
  
imports: from __future__ import division
  import numbers
  from abc import ABCMeta
  from abc import abstractmethod
  from math import ceil
  import numpy
  from scipy.sparse import issparse
  from ..base import BaseEstimator
  from ..base import ClassifierMixin
  from ..base import RegressorMixin
  from ..externals import six
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..utils import check_array
  from ..utils import check_random_state
  from ..utils import compute_sample_weight
  from ..utils.multiclass import check_classification_targets
  from ..exceptions import NotFittedError
  from ._criterion import Criterion
  from ._splitter import Splitter
  from ._tree import DepthFirstTreeBuilder
  from ._tree import BestFirstTreeBuilder
  from ._tree import Tree
  from .None import _tree, _splitter, _criterion
label_desc: Use image node_id 1 to create a new BaseDecisionTree object from inherited base classes:  with example: obj = BaseDecisionTree(criterion, splitter, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, random_state, min_impurity_split, class_weight, presort)