November 2018
Beginner to intermediate
182 pages
4h 48m
English
Content preview from Natural Language Processing with Python Quick Start Guide
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Removing stop words
Let's now remove the stop words for English again, by simply passing english to the tokenizer as follows:
mnb_clf = Pipeline([('vect', CountVectorizer(stop_words='english')), ('tfidf', TfidfTransformer()), ('clf',MNB())])mnb_clf.fit(X=X_train, y=y_train)mnb_acc, mnb_predictions = imdb_acc(mnb_clf)mnb_acc # 0.82992
This helps improve performance, but only marginally. We might be better off simply keeping in the stop words for other classifiers that we try.
As a last manual experiment, let's try adding bigrams and unigrams, as we did lfor ogistic regression, as follows:
mnb_clf = Pipeline([('vect', CountVectorizer(stop_words='english', ngram_range=(1,3))), ('tfidf', TfidfTransformer()), ('clf',MNB())])mnb_clf.fit(X=X_train, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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