class_docstr: This object provides the primary interface to the functionality of this library. You can think of it as
  an adaptor that adapts the various decision-tree based libraries for use with dtreeviz. In implementation,
  however, this object encapsulates the key functionality and API but delegates tree model adaptation
  from sklearn etc... to dtreeviz.models.ShadowDecTree subclasses.
class_docstr_tok: ["This","object","provides","the","primary","interface","to","the","functionality","of","this","library",".","You","can","think","of","it","as","an","adaptor","that","adapts","the","various","decision-tree","based","libraries","for","use","with","dtreeviz",".","In","implementation",",","however",",","this","object","encapsulates","the","key","functionality","and","API","but","delegates","tree","model","adaptation","from","sklearn","etc","...","to","dtreeviz.models.ShadowDecTree","subclasses","."]
docstr: Prediction path interpretation for a data instance. There will be created a range of values for each feature,
   based on data instance values and its tree prediction path.
  
  A possible output for this method could be :
      1.5 <= Pclass
      3.5 <= Age < 44.5
      7.91 <= Fare < 54.25
      0.5 <= Sex_label
      Cabin_label < 3.5
      0.5 <= Embarked_label
  
  Usage example :
  viz_model = dtreeviz.model(tree_model, X_train=dataset[features], y_train=dataset[target],
                             feature_names=features, target_name=target, class_names=[0, 1])
  viz_model.explain_prediction_path(x)
  
  :param x: np.ndarray
      The data instance for which we want to investigate prediction path
code: def explain_prediction_path(self, x: np.ndarray) -> str:
      """Prediction path interpretation for a data instance. There will be created a range of values for each feature,
       based on data instance values and its tree prediction path.
  
      A possible output for this method could be :
          1.5 <= Pclass
          3.5 <= Age < 44.5
          7.91 <= Fare < 54.25
          0.5 <= Sex_label
          Cabin_label < 3.5
          0.5 <= Embarked_label
  
      Usage example :
      viz_model = dtreeviz.model(tree_model, X_train=dataset[features], y_train=dataset[target],
                                 feature_names=features, target_name=target, class_names=[0, 1])
      viz_model.explain_prediction_path(x)
  
      :param x: np.ndarray
          The data instance for which we want to investigate prediction path
      """
      return explain_prediction_plain_english(self.shadow_tree, x)
  
imports: import os
  import tempfile
  from typing import Mapping, List, Callable
  import matplotlib
  import matplotlib.patches
  import matplotlib.pyplot
  import numpy
  import pandas
  from colour import Color, rgb2hex
  from sklearn import tree
  from dtreeviz.colors import adjust_colors
  from dtreeviz.interpretation import explain_prediction_plain_english, explain_prediction_sklearn_default
  from dtreeviz.models.shadow_decision_tree import ShadowDecTree
  from dtreeviz.models.shadow_decision_tree import ShadowDecTreeNode
  from dtreeviz.utils import myround, DTreeVizRender, add_classifier_legend, _format_axes, _draw_wedge, _set_wedge_ticks, tessellate, is_numeric
label_desc: Use image node_id 8 for calling the DTreeVizAPI obj's underlying member method code with example usage: obj.explain_prediction_path(x) and returns: explain_prediction_plain_english