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Exercise

Implementing the hashing trick in scikit-learn

In this exercise you will check out the scikit-learn implementation of HashingVectorizer before adding it to your pipeline later.

As you saw in the video, HashingVectorizer acts just like CountVectorizer in that it can accept token_pattern and ngram_range parameters. The important difference is that it creates hash values from the text, so that we get all the computational advantages of hashing!

Instructions

100 XP
  • Import HashingVectorizer from sklearn.feature_extraction.text.
  • Instantiate the HashingVectorizer as hashing_vec using the TOKENS_ALPHANUMERIC pattern.
  • Fit and transform hashing_vec using text_data. Save the result as hashed_text.
  • Hit submit to see some of the resulting hash values.