class_docstr: Classification tree with shallow depth.
class_docstr_tok: ["Classification","tree","with","shallow","depth","."]
docstr: Probability estimates on provided instances.
  
  Args:
      X: Numpy array for instances.
  
  Returns:
      Probability estimate of instance for each class.
docstr_tok: ["Probability","estimates","on","provided","instances",".","Args",":","X",":","Numpy","array","for","instances",".","Returns",":","Probability","estimate","of","instance","for","each","class","."]
code: def predict_proba(self, X):
      """Probability estimates on provided instances.
  
      Args:
          X: Numpy array for instances.
  
      Returns:
          Probability estimate of instance for each class.
      """
  
      check_is_fitted(self, "has_fitted_")
  
      X, n_samples = preclean_X(
          X, self.feature_names_in_, self.feature_types_in_
      )
      X, _, _ = unify_data(
          X,
          n_samples,
          self.feature_names_in_,
          self.feature_types_in_,
          False,
          0,
      )
  
      return self._model().predict_proba(X)
  
imports: from ..api.base import ExplainerMixin, ExplanationMixin
  from ..utils._explanation import gen_name_from_class, gen_local_selector, gen_global_selector, gen_perf_dicts
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.base import is_classifier
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from abc import abstractmethod
  from sklearn.tree import _tree
  from copy import deepcopy
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions, typify_classification
  from ..utils._unify_data import unify_data
  import logging
label_desc: Use image node_id 4 for calling the ClassificationTree obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self