Since I also have the result from count vectorizer, I tried in the previous post, I will plot them together on the same graph to compare.From above chart, we can see including bigram and trigram boost the model performance both in count vectorizer and TFIDF vectorizer. And for every case...
{'Document':' '.join})# Create c-TF-IDF based on the train datacount_vectorizer=CountVectorizer().fit(docs_per_class.Document)count=count_vectorizer.transform(docs_per_class.Document)ctfidf_vectorizer=CTFIDFVectorizer().fit(count,n_samples=len(docs))ctfidf=ctfidf_vectorizer.transform(count...