title: How to fix the error "For multi-metric scoring" for OneClassSVM and GridSearchCV
body: <p>I am trying to use OnClassSVM for anomaly detection purpose and I tuned its parameters using GridSearchCV() as follows:</p>
  
  <p>I have searched many sites for it including <a href="https://stackoverflow.com/">https://stackoverflow.com/</a> but could not find any proper solution of my scenario.
  code is here: </p>
  
  <pre><code>nus = [0.001, 0.01, 0.1, 1]
  gammas = [0.001, 0.01, 0.1, 1]
  scorers = {
    'precision_score': make_scorer(precision_score),
    'recall_score': make_scorer(recall_score),
   'accuracy_score': make_scorer(accuracy_score)
  }
  
  tuned_parameters = {'C': [1, 10, 100, 1000], 'kernel' : ['rbf','linear'], 
  'gamma' : gammas, 'nu': nus}
  tuned_ocsvm = svm.OneClassSVM()
  ocsvm = GridSearchCV(estimator=svm.OneClassSVM(), 
  param_grid=tuned_parameters, scoring=scorers,refit='false')
  </code></pre>
  
  <p>But it is giving me error as follows</p>
  
  <blockquote>
    <p>For multi-metric scoring, the parameter refit must be set to a scorer key or a callable to refit an estimator with the best parameter setting on the whole data and make the best_* attributes available for that metric. If this is not needed, refit should be set to False explicitly. 'false' was passed</p>
  </blockquote>