Hyperparameter Tuning with RandomizedSearchCV

Grid search goes over the entire search space and trains a model or estimator for every combination of parameters. Randomized search goes over only some of the combinations. This is a more optimal use of resources and still provides the benefits of hyperparameter tuning and cross-validation. You will be looking at this in depth in Chapter 8, Hyperparameter Tuning.

Have a look at the following exercise.

Exercise 7.08: Using Randomized Search for Hyperparameter Tuning

The goal of this exercise is to perform hyperparameter tuning using randomized search and cross-validation.

The following steps will help you complete this exercise:

1. Open a new Colab notebook file.
2. Import pandas:

   import pandas as pd

   In this step, you import pandas. You will make use of it in the next step.

3. Create headers:

   _headers = ['buying', 'maint', 'doors', 'persons', \
         ...