title: What is Meaning of mean_score_time in scikit-learn Grid Search Cross Validation
body: <p>I am a bit confused with the meaning of <code>mean_score_time</code> in <a href="http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html" rel="nofollow noreferrer">GridSearch</a>.</p>
  
  <p>For the <code>mean_fit_time</code> it's clear that it's the average time of training between different folds.
  But in case of <code>mean_score_time</code> I don't know whether it's the time taken by each <code>Cross-Validation</code> set to be classified or what?</p>
  
  <p>I tried to compute the time for the test set </p>
  
  <pre><code>start = time.time()
  y_tst_pred = estimator.predict(X_test)
  end = time.time()
  print(end-start)
  </code></pre>
  
  <p>and the results came much smaller although the test set is larger and should give larger time.</p>
  
  <blockquote>
    <p>To avoid confusion: My question has two main parts:</p>
  </blockquote>
  
  <p>1 - What is the <code>mean_score_time</code>.. I know its logical to assume that it is the mean classification time across different folds ... but my second part is the reason for confusion.</p>
  
  <p>2 - Assuming that <code>mean_score_time</code> is the time needed for evaluation. why the time used for classifying the test set (test set approx = 550 sample) is lower than the <code>mean_score_time</code> (cross validation approx = 440).. <code>mean_score_time</code>=56 ms and the time measured in clustering the test set = 35 ms.</p>
  
  <p>P.s. Some of the answers suggest <code>mean_score_time</code> contains classification and scoring time (comparing the predicted results and labels) but none of the documentations say something like this.</p>