8. Hyperparameter Tuning

Overview

In this chapter, each hyperparameter tuning strategy will be first broken down into its key steps before any high-level scikit-learn implementations are demonstrated. This is to ensure that you fully understand the concept behind each of the strategies before jumping to the more automated methods.

By the end of this chapter, you will be able to find further predictive performance improvements via the systematic evaluation of estimators with different hyperparameters. You will successfully deploy manual, grid, and random search strategies to find the optimal hyperparameters. You will be able to parameterize k-nearest neighbors (k-NN), support vector machines (SVMs), ridge regression, and random forest classifiers to optimize model performance.