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@ken333135
Created May 23, 2018 02:18
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#Define a pipeline combining a text feature extractor with multi label classifer
NB_pipeline = Pipeline([
('tfidf',TfidfVectorizer()),
('clf', OneVsRestClassifier(MultinomialNB(fit_prior=True,class_prior=None))),
])
#Train the model and get the prediction
score = []
for code in label:
# train the model
NB_pipeline.fit(x_train,train[code])
#compute the testing accuracy
prediction = NB_pipeline.predict(x_test)
score.append(roc_auc_score(test[code],prediction))
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