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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

Cet exercice fait partie du cours

Hyperparameter Tuning in Python

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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(____)
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