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  3. Hyperparameter Tuning in Python

Exercise

Genetic Hyperparameter Tuning with TPOT

You're going to undertake a simple example of genetic hyperparameter tuning. TPOT is a very powerful library that has a lot of features. You're just scratching the surface in this lesson, but you are highly encouraged to explore in your own time.

This is a very small example. In real life, TPOT is designed to be run for many hours to find the best model. You would have a much larger population and offspring size as well as hundreds more generations to find a good model.

You will create the estimator, fit the estimator to the training data and then score this on the test data.

For this example we wish to use:

  • 3 generations
  • 4 in the population size
  • 3 offspring in each generation
  • accuracy for scoring

A random_state of 2 has been set for consistency of results.

Instructions

100 XP
  • Assign the values outlined in the context to the inputs for tpot_clf.
  • Create the tpot_clf classifier with the correct inputs.
  • Fit the classifier to the training data (X_train & y_train are available in your workspace).
  • Use the fitted classifier to score on the test set (X_test & y_test are available in your workspace).