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: Validate the training data on predict (probabilities).
docstr_tok: ["Validate","the","training","data","on","predict","(","probabilities",")","."]
code: def _validate_X_predict(self, X, check_input):
      """Validate the training data on predict (probabilities)."""
      if check_input:
          X = self._validate_data(
              X, dtype=DTYPE, accept_sparse="csr", reset=False
          )
          if issparse(X) and (
              X.indices.dtype != np.intc or X.indptr.dtype != np.intc
          ):
              raise ValueError(
                  "No support for np.int64 index based sparse matrices"
              )
      else:
          # The number of features is checked regardless of `check_input`
          self._check_n_features(X, reset=False)
      return 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 5 for calling the BaseDecisionTree obj's underlying member method code with example usage: obj._validate_X_predict(X, check_input) and returns: X