title: how to assess the confidence score of a prediction with scikit-learn
body: <p>I have write down a simple code that takes One arguments "query_seq", further methods calculates descriptor and in the end predictions can be made using  "LogisticRegression" (or any other algorithm provided with the function) algorithms as "0 (negative for given case)" or "1 (positive for given case)"</p>
  
  <pre><code>def main_process(query_Seq):
      LR = LogisticRegression()
      GNB = GaussianNB()
      KNB = KNeighborsClassifier()
      DT = DecisionTreeClassifier()
      SV = SVC(probability=True)
  
      train_x, train_y,train_l = data_gen(p) 
      a  = DC_CLASS()
      test_x = a.main_p(query_Seq)
      return Prediction(train_x, train_y, test_x,LR)
  </code></pre>
  
  <p>While we performed cross validation we have calculated the different statistical parameters for the accuracy estimation (specificity, sensitivity, mmc, etc. ) for an algorithm. Now my Question is that, is there any method in scikit-learn through which we can estimate the confidence score for a test data prediction. </p>