docs: def scores_to_probs(scores, proba, eps=0.01):
      """Transforms scores to probabilities by applying the logistic function"""
      if np.any(~proba):
          # Need to convert some of the scores into probabilities
          probs = copy.deepcopy(scores)
          n_class = len(proba)
          for m in range(n_class):
              if not proba[m]:
                  #TODO: incorporate threshold (currently assuming zero)
                  # find most extreme absolute score
                  max_extreme_score = max(np.abs(np.min(scores[:,m])),\
                                      np.abs(np.max(scores[:,m])))
                  k = np.log((1-eps)/eps)/max_extreme_score # scale factor
                  self._probs[:,m] = expit(k * self.scores[:,m])
          return probs
      else:
          return scores