title: How to compute different metric value in python cross validation
body: <p>I am using python sk-learn library for classification. I am using cross validation for finding effectiveness of the classification algorithm. I want to compute accuracy, precision, recall, F1 measure. Currently I am using following code.</p>
  
  <pre><code>           dt  =  DecisionTreeClassifier(max_depth=dt_est)
             dt_acc = cross_validation.cross_val_score(dt,x_data_tfidf.toarray(), target_arr, cv=cv,                  scoring='accuracy')
             dt_f1 = cross_validation.cross_val_score(dt,x_data_tfidf.toarray(), target_arr, cv=cv,scoring='f1')
             dt_pre = cross_validation.cross_val_score(dt,x_data_tfidf.toarray(), target_arr, cv=cv, scoring='precision')     
             dt_re = cross_validation.cross_val_score(dt,x_data_tfidf.toarray(), target_arr, cv=cv, scoring='recall')    
  </code></pre>
  
  <p>I want ask is there any way using which I can get all (accuracy, precision, recall, f1) in single computation, currently I have to compute all the metric individually.</p>
  
  <p>Thanks! in advance.</p>