docstr: [Create prediction columns and add them to the self.predicted_columns dictionary]
  Args:
      model
      x_test: 
      y_test
      ColumnTransformer
docstr_tok: ["[","Create","prediction","columns","and","add","them","to","the","self.predicted_columns","dictionary","]","Args",":","model","x_test",":","y_test","ColumnTransformer"]
code: def create_prediction_columns(self):
      """[Create prediction columns and add them to the self.predicted_columns dictionary]
      Args:
          model
          x_test:
          y_test
          ColumnTransformer
      """
      if is_classification(self.model) == True:
          prediction, probabilities = self.make_predictions()
          self.predicted_columns["Model Decision"] = prediction
          self.predicted_columns["True Values"] = self.target_data
          for i in range(len(np.unique(prediction))):
              self.predicted_columns[
                  "Probability: {}".format(np.unique(prediction)[i])
              ] = probabilities[:, i]
  
      else:
          prediction = self.make_predictions()
          self.predicted_columns["Model Decision"] = prediction
          self.predicted_columns["True Values"] = self.target_data
  
file_path: explainx/lib/models/modelprocessor.py
imports: from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from typing import Dict, List, Union
  from sklearn.impute import SimpleImputer
  from explainx.lib.utils import is_classification
label_desc: Use image node_id 8 for calling the ModelProcessor obj's underlying member method code with example usage: obj.create_prediction_columns() without return types