Exercise

Generate a tuning grid

The standard hyperparameters of most models provide a good fit for most datasets. Yet, they need optimization for the best performance. Otherwise, it's like driving a car with an activated hand brake. Release the brake and tune your models!

In this exercise, you'll create two objects that serve as a starting point: a tuning grid (a set of hyperparameter combinations) and a model specification that you later train with every value of the grid.

Instructions 1/2

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Create an rpart powered classification tree specification, flagging the tree_depth and cost_complexity parameters for tuning.