Chapter 8: Hyperparameter Tuning via Hyperopt

Hyperopt is an optimization package in Python that provides several implementations of hyperparameter tuning methods, including Random Search, Simulated Annealing (SA), Tree-Structured Parzen Estimators (TPE), and Adaptive TPE (ATPE). It also supports various types of hyperparameters with ranging types of sampling distributions.

In this chapter, we’ll introduce the Hyperopt package, starting with its capabilities and limitations, how to utilize it to perform hyperparameter tuning, and all the other important things you need to know about Hyperopt. We’ll learn not only how to utilize Hyperopt to perform hyperparameter tuning with its default configurations but also discuss the available configurations, along with their usage. Moreover, we’ll discuss how the implementation of the hyperparameter tuning methods is related to the theory that we learned about in the previous chapters, since there some minor differences...