code: def randomforest_shap_classification(self, model, X, prediction_col):
      explainer = shap.TreeExplainer(model)
      shap_values = explainer.shap_values(X, approximate=True)
  
      pd_shap = self.select_row_shap_values(shap_values, prediction_col)
      all_columns = list(X.columns)
  
      pd_shap.columns = [f"{y}_impact" for y in all_columns]
  
      shap_columns = pd_shap.columns
  
      Y = X.copy()
      for c in shap_columns:
          Y[c] = list(pd_shap[c])
  
      return Y, explainer
  
file_path: explainx/lib/calculate_shap.py
imports: from imports import *
  from rescale_numeric_feature import *
label_desc: Use image node_id 8 for calling the calculate_shap obj's underlying member method code with example usage: obj.randomforest_shap_classification(model, X, prediction_col) and returns: Y,  explainer