Building Machine Learning Systems with Python - Third Edition

Tuning the classifier's parameters

Certainly, we have not explored the current setup enough and should investigate more. There are roughly two areas where we can play with the knobs: TfidfVectorizer and MultinomialNB. As we have no real intuition in which area we should explore, let's try to sweep the hyperparameters.

We will see the TfidfVectorizer parameter first:

• Using different settings for ngrams:
  • unigrams (1,1)
  • unigrams and bigrams (1,2)
  • unigrams, bigrams, and trigrams (1,3)
• Playing with min_df: 1 or 2
• Exploring the impact of IDF within TF-IDF using use_idf and smooth_idf: False or True
• Whether to remove stop words or not, by setting stop_words to english or None
• Whether to use the logarithm of the word counts (sublinear_tf)
• Whether ...