code: def get_confidence_value(algorithm,model,grader_feats,score, scores):
      
      min_score=min(numpy.asarray(scores))
      max_score=max(numpy.asarray(scores))
      if algorithm == util_functions.AlgorithmTypes.classification and hasattr(model, "predict_proba"):
          #If classification, predict with probability, which gives you a matrix of confidences per score point
          raw_confidence=model.predict_proba(grader_feats)[0,(float(score)-float(min_score))]
          #TODO: Normalize confidence somehow here
          confidence=raw_confidence
      elif hasattr(model, "predict"):
          raw_confidence = model.predict(grader_feats)[0]
          confidence = max(float(raw_confidence) - math.floor(float(raw_confidence)), math.ceil(float(raw_confidence)) - float(raw_confidence))
      else:
          confidence = 0
  
      return confidence
docs: Determines a confidence in a certain score, given proper input parameters
      algorithm- from util_functions.AlgorithmTypes
      model - a trained model
      grader_feats - a row of features used by the model for classification/regression
      score - The score assigned to the submission by a prior model