sohow would the naive bayes classifier perform on classifying positive tweets versus the rest and negative tweets versus the restone wordbad =pos vs rest = =neg vs rest = pretty unusable if you ask me looking at the / curves shown in the following screenshotswe also find no usable precision/recall tradeoff as we were able to do in the previous tuning the classifier' parameters certainlywe have not explored the current setup enough and should investigate more there are roughly two areas where we could play with the knobstfidfvectorizer and multinomialnb as we have no real intuition as to which area we should explorelet us try to distribute the parametersvaluestfidfvectorizer degdeg use different settings for ngramsunigrams ( , )bigrams ( , )and trigrams ( , degdeg play with min_df or degdeg explore the impact of idf within tf-idf using use_idf and smooth_idffalse or true degdeg play with the idea of whether to remove stop words or not by setting stop_words to english or none