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a guest Jun 20th, 2019 51 Never
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  1. classifer = clf()
  2. classifer.fit(x_train, y_train)
  3. predict = sklearn.model_selection.cross_val_predict(classifer, x_test, y_test, cv=10)
  4.  
  5. scores = sklearn.model_selection.cross_val_score(classifer, x_test, y_test, cv=10)
  6. print (scores)
  7. print ("Accurecy of %s: %0.2f (+/- %0.2f)" % (classifer, scores.mean(), scores.std() *2))
  8. print (classification_report(y_test, predict))
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