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","."]
docstr: Return the decision path in the tree.
  
  .. versionadded:: 0.18
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csr_matrix``.
  
  check_input : bool, default=True
      Allow to bypass several input checking.
      Don't use this parameter unless you know what you do.
  
  Returns
  -------
  indicator : sparse matrix of shape (n_samples, n_nodes)
      Return a node indicator CSR matrix where non zero elements
      indicates that the samples goes through the nodes.
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code: def decision_path(self, X, check_input=True):
      """Return the decision path in the tree.
  
      .. versionadded:: 0.18
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csr_matrix``.
  
      check_input : bool, default=True
          Allow to bypass several input checking.
          Don't use this parameter unless you know what you do.
  
      Returns
      -------
      indicator : sparse matrix of shape (n_samples, n_nodes)
          Return a node indicator CSR matrix where non zero elements
          indicates that the samples goes through the nodes.
      """
      X = self._validate_X_predict(X, check_input)
      return self.tree_.decision_path(X)
  
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 8 for calling the BaseDecisionTree obj's underlying member method code with example usage: obj.decision_path(X, check_input) and returns: self