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  3. Hyperparameter Tuning in Python

Exercise

Randomly Search with Random Forest

To solidify your knowledge of random sampling, let's try a similar exercise but using different hyperparameters and a different algorithm.

As before, create some lists of hyperparameters that can be zipped up to a list of lists. You will use the hyperparameters criterion, max_depth and max_features of the random forest algorithm. Then you will randomly sample hyperparameter combinations in preparation for running a random search.

You will use a slightly different package for sampling in this task, random.sample().

Instructions

100 XP
  • Create lists of the values 'gini' and 'entropy' for criterion & "auto", "sqrt", "log2", None for max_features.
  • Create a list of values between 3 and 55 inclusive for the hyperparameter max_depth and assign to the list max_depth_list. Remember that range(N,M) will create a list from N to M-1.
  • Combine these lists into a list of lists to sample from using product().
  • Randomly sample 150 models from the combined list and print the result.