Exercise

Tuning multiple hyperparameters

In this exercise, you will practice tuning multiple hyperparameters simultaneously. This is a valuable topic to learn, as hyperparameters of an algorithm usually affect each other's values. Therefore, tuning them individually is not usually the recommended course of action.

You will tune the max_features and max_samples parameters of IForest using a sample of the Big Mart sales data.

IForest and airbnb_df are already loaded for you. The product function from itertools is also available.

Instructions 1/3

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Create a list for max_features with the values 0.6, 0.8, and 1; and a list for max_samples with the values 0.8, 0.9, 1.