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Exercise

Training a spam classifier

The SMS data have now been prepared for building a classifier. Specifically, this is what you have done:

  • removed numbers and punctuation
  • split the messages into words (or "tokens")
  • removed stop words
  • applied the hashing trick and
  • converted to a TF-IDF representation.

Next you'll need to split the TF-IDF data into training and testing sets. Then you'll use the training data to fit a Logistic Regression model and finally evaluate the performance of that model on the testing data.

The data are stored in sms and LogisticRegression has been imported for you.

Instructions
100 XP
  • Split the data into training and testing sets in a 4:1 ratio. Set the random number seed to 13 to ensure repeatability.
  • Create a LogisticRegression object and fit it to the training data.
  • Generate predictions on the testing data.
  • Use the predictions to form a confusion matrix.
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