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

Chapter 3: Exploring Exhaustive Search

Hyperparameter tuning doesn't always correspond to fancy and complex search algorithms. In fact, a simple for loop or manual search based on the developer's instinct can also be utilized to achieve the goal of hyperparameter tuning, which is to get the maximum evaluation score on the validation score without causing an overfitting issue.

In this chapter, we'll discuss the first out of four groups of hyperparameter tuning, called an exhaustive search. This is the most widely used and most straightforward hyperparameter-tuning group in practice. As explained by its name, hyperparameter-tuning methods that belong to this group work by exhaustively searching through the hyperparameter space. Except for one method, all of the methods in this group are categorized as uninformed search algorithms, meaning they are not learning from previous iterations to have a better search space in the future. Three methods will be discussed in this...