Random grid search
The most common method of hyperparameter tuning is grid search. This method creates a tuning grid with unique combinations of hyperparameter values and uses cross validation to evaluate their performance. The goal of hyperparameter tuning is to find the optimal combination of values for maximizing model performance.
In this exercise, you will create a random hyperparameter grid and tune your loans data decision tree model.
Your cross validation folds, loans_folds, workflow object, loans_tune_wkfl, custom metrics function, loans_metrics, and dt_tune_model have been loaded into your session.
This exercise is part of the course
Modeling with tidymodels in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Hyperparameter tuning with grid search
set.seed(214)
dt_grid <- ___(___(___),
size = ___)
dt_grid