Iteratively tune multiple hyperparameters
In this exercise, you will build on the function you previously created to take in 2 hyperparameters, build a model and return the results. You will now use that to loop through some values and then extend this function and loop with another hyperparameter.
The function gbm_grid_search(learn_rate, max_depth) is available in this exercise.
If you need to remind yourself of the function you can run the function print_func() that has been created for you
This exercise is part of the course
Hyperparameter Tuning in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the relevant lists
results_list = ____
learn_rate_list = ____
max_depth_list = ____
# Create the for loop
for learn_rate in ____:
for max_depth in ____:
____.append(gbm_grid_search(____,____))
# Print the results
print(____)