Exercise

Creating Hyperparameters

For a school assignment, your professor has asked your class to create a random forest model to predict the average test score for the final exam.

After developing an initial random forest model, you are unsatisfied with the overall accuracy. You realize that there are too many hyperparameters to choose from, and each one has a lot of possible values. You have decided to make a list of possible ranges for the hyperparameters you might use in your next model.

Your professor has provided de-identified data for the last ten quizzes to act as the training data. There are 30 students in your class.

Instructions 1/3

XP

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Print.get_params() in the console to review the possible parameters of the model that you can tune.