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().

Create lists of the valid values for the criterion & max_features hyperparameters, testing all valid values (don't use an int or float for max_features - test all other values).
Create a list of values between 3 and 55 inclusive for the hyperparameter max_depth and assign to the list max_depth_list.
Combine these lists into a list of lists to sample from.
Randomly sample 150 models from these hyperparameter combinations and print the result.

Take Hint (-30 XP)

Exercise

Randomly Search with Random Forest

Instructions