code: def _get_value(scikit_value, mode = 'regressor', scaling = 1.0, n_classes = 2, tree_index = 0):
      
      # Regression
      if mode == 'regressor':
          return scikit_value[0] * scaling
  
      # Binary classification
      if n_classes == 2:
          # Decision tree
          if len(scikit_value[0]) != 1:
              value = scikit_value[0][1] * scaling / scikit_value[0].sum()
          # boosted tree
          else:
              value = scikit_value[0][0] * scaling
          if value == 0.5:
              value = value - 1e-7
  
      # Multiclass classification
      else:
          # Decision tree
          if len(scikit_value[0]) != 1:
              value = scikit_value[0] / scikit_value[0].sum()
          # boosted tree
          else:
              value = {tree_index: scikit_value[0] * scaling}
      return value
docs: Get the right value from the scikit-tree