Randomly Sample Hyperparameters

To undertake a random search, we firstly need to undertake a random sampling of our hyperparameter space.

In this exercise, you will firstly create some lists of hyperparameters that can be zipped up to a list of lists. Then you will randomly sample hyperparameter combinations in preparation for running a random search.

You will use just the hyperparameters learning_rate and min_samples_leaf of the GBM algorithm to keep the example illustrative and not overly complicated.

Create a list of 200 values for the learning_rate hyperparameter between 0.01 and 1.5 and assign to the list learn_rate_list.
Create a list of values between 10 and 40 inclusive for the hyperparameter min_samples_leaf and assign to the list min_samples_list.
Combine these lists into a list of lists to sample from.
Randomly sample 250 models from these hyperparameter combinations and print the result.

Exercise

Randomly Sample Hyperparameters

Instructions