class_docstr: Provides 'Tree Explainer' algorithm for specific sklearn trees.
  
  Wrapper around andosa/treeinterpreter github package.
  
  https://github.com/andosa/treeinterpreter
  
  Currently supports (copied from README.md):
  
  - DecisionTreeRegressor
  - DecisionTreeClassifier
  - ExtraTreeRegressor
  - ExtraTreeClassifier
  - RandomForestRegressor
  - RandomForestClassifier
  - ExtraTreesRegressor
  - ExtraTreesClassifier
docstr: Initializes class.
  
  Args:
      model: A scikit-learn tree object
      data: mostly ignored. Only included for conformance to the greybox API
            if data is provided though we use it to determine the feature names and types
      feature_names: List of feature names.
      feature_types: List of feature types.
code: def __init__(
      self,
      model,
      data=None,
      feature_names=None,
      feature_types=None,
  ):
      """Initializes class.
  
      Args:
          model: A scikit-learn tree object
          data: mostly ignored. Only included for conformance to the greybox API
                if data is provided though we use it to determine the feature names and types
          feature_names: List of feature names.
          feature_types: List of feature types.
      """
  
      self.model = model
      self.feature_names = feature_names
      self.feature_types = feature_types
  
      self.feature_names_in_ = None
      self.feature_types_in_ = None
  
      if data is not None:
          # if the user provides data, we use it as a larger corpus than X
          data, n_samples = preclean_X(
              data, feature_names, feature_types
          )
  
          (
              _,
              self.feature_names_in_,
              self.feature_types_in_,
          ) = unify_data(
              data, n_samples, feature_names, feature_types, False, 0
          )
  
file_path: interpret/python/interpret-core/interpret/greybox/_treeinterpreter.py
imports: from ..api.base import ExplainerMixin
  from ..api.templates import FeatureValueExplanation
  from ..utils._explanation import gen_name_from_class, gen_perf_dicts, gen_local_selector
  import numpy
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions, typify_classification
  from ..utils._unify_predict import determine_classes, unify_predict_fn
  from ..utils._unify_data import unify_data
label_desc: Use image node_id 1 to create a new TreeInterpreter object from inherited base classes: ExplainerMixin with example: obj = TreeInterpreter(model, data, feature_names, feature_types)