Book Image

Hyperparameter Tuning with Python

By : Louis Owen
Book Image

Hyperparameter Tuning with Python

By: Louis Owen

Overview of this book

Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.
Table of Contents (19 chapters)
Section 1:The Methods
Section 2:The Implementation
Section 3:Putting Things into Practice


In this chapter, we discussed the second out of four groups of hyperparameter tuning methods, called the BO group. We not only discussed BO in general but also several of its variants, including BOGP, SMAC, TPE, and Metis. We saw what makes each of the variants differ from each other, along with the pros and cons of each. At this point, you should be able to explain BO with confidence when someone asks you and apply hyperparameter tuning methods in this group with ease.

In the next chapter, we will start discussing heuristic search, the third group of hyperparameter tuning methods. The goal of the next chapter is similar to this chapter: to provide a better understanding of the methods that belong to the heuristic search group.