class_docstr: An explanation for classifier or regressor,
  it can either explain weights or a single prediction.
class_docstr_tok: ["An","explanation","for","classifier","or","regressor",",","it","can","either","explain","weights","or","a","single","prediction","."]
code: def __init__(
      self,
      estimator,  # type: str
      description=None,  # type: Optional[str]
      error=None,  # type: Optional[str]
      method=None,  # type: Optional[str]
      is_regression=False,  # type: bool
      targets=None,  # type: Optional[List[TargetExplanation]]
      feature_importances=None,  # type: Optional[FeatureImportances]
      decision_tree=None,  # type: Optional[TreeInfo]
      highlight_spaces=None,  # type: Optional[bool]
      transition_features=None,  # type: Optional[TransitionFeatureWeights]
      image=None,  # type: Any
  ):
      # type: (...) -> None
      self.estimator = estimator
      self.description = description
      self.error = error
      self.method = method
      self.is_regression = is_regression
      self.targets = targets
      self.feature_importances = feature_importances
      self.decision_tree = decision_tree
      self.highlight_spaces = highlight_spaces
      self.transition_features = transition_features
      self.image = image
  
imports: from typing import Any, List, Tuple, Union, Optional
  import numpy
  from .base_utils import attrs
  from .formatters.features import FormattedFeatureName
label_desc: Use image node_id 1 to create a new Explanation object from inherited base classes: object with example: obj = Explanation(estimator, description, error, method, is_regression, targets, feature_importances, decision_tree, highlight_spaces, transition_features, image)