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- def multinomial_nb_with_cv(x_train, y_train):
- random.shuffle(X)
- kf = cross_validation.KFold(len(X), n_folds=10)
- acc = []
- for train_index, test_index in kf:
- y_true = y_train[test_index]
- clf = MultinomialNB().fit(x_train[train_index],
- y_train[train_index])
- y_pred = clf.predict(x_train[test_index])
- acc.append(accuracy_score(y_true, y_pred))
- def multinomial_nb(x_train, y_train, x_test, y_test):
- clf = MultinomialNB().fit(x_train, y_train)
- y_pred = clf.predict(x_test)
- y_true = y_test
- print classification_report(y_true, y_pred)
- precision recall f1-score support
- 0 0.50 0.24 0.33 221
- 1 0.00 0.00 0.00 18
- 2 0.00 0.00 0.00 27
- 3 0.00 0.00 0.00 28
- 4 0.00 0.00 0.00 32
- 5 0.04 0.02 0.02 57
- 6 0.00 0.00 0.00 26
- 7 0.00 0.00 0.00 25
- 8 0.00 0.00 0.00 43
- 9 0.00 0.00 0.00 99
- 10 0.63 0.98 0.76 716
- avg / total 0.44 0.59 0.48 1292
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