class_docstr: Handles explanations that can be visualized as horizontal bar graphs.
  Usually these are feature-value pairs being represented.
class_docstr_tok: ["Handles","explanations","that","can","be","visualized","as","horizontal","bar","graphs",".","Usually","these","are","feature-value","pairs","being","represented","."]
docstr: Initializes class.
  
  Args:
      explanation_type:  Type of explanation.
      internal_obj: A jsonable object that backs the explanation.
      feature_names: List of feature names.
      feature_types: List of feature types.
      name: User-defined name of explanation.
      selector: A dataframe whose indices correspond to explanation entries.
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code: def __init__(
      self,
      explanation_type,
      internal_obj,
      feature_names=None,
      feature_types=None,
      name=None,
      selector=None,
  ):
      """Initializes class.
  
      Args:
          explanation_type:  Type of explanation.
          internal_obj: A jsonable object that backs the explanation.
          feature_names: List of feature names.
          feature_types: List of feature types.
          name: User-defined name of explanation.
          selector: A dataframe whose indices correspond to explanation entries.
      """
      self.explanation_type = explanation_type
      self._internal_obj = internal_obj
      self.feature_names = feature_names
      self.feature_types = feature_types
      self.name = name
      self.selector = selector
  
imports: import interpret.api.base
label_desc: Use image node_id 1 to create a new FeatureValueExplanation object from inherited base classes: interpret.api.base with example: obj = FeatureValueExplanation(explanation_type, internal_obj, feature_names, feature_types, name, selector)