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 have discussed the first out of four groups of hyperparameter-tuning methods, called the exhaustive search group. We have discussed manual search, grid search, and random search. We not only discussed the definition of those methods, but also how those methods work at both a high level and a technical level, and what are the pros and cons for each of them. From now on, you should be able to explain these exhaustive search methods with confidence when someone asks you about them and apply all of the exhaustive search methods with high confidence in practice.

In the next chapter, we will start discussing Bayesian optimization, the second group of hyperparameter-tuning methods. The goal of the next chapter is similar to this chapter, which is to give a better understanding of methods belonging to the Bayesian optimization group so that you can utilize those methods with high confidence in practice.