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

Understanding genetic algorithms

GAs are popular heuristic search methods that are inspired by Charles Darwin’s theory of natural selection. Unlike SA, which is classified as a single-point-based heuristic search method, GAs are categorized as population-based methods since they maintain a group of possible candidate solutions instead of just a single candidate solution at each trial. As a hyperparameter tuning method, you are recommended to utilize a GA when each trial doesn’t take too much time and you have enough computational resources, such as parallel computing resources.

To have a better understanding of GAs, let’s start with a simple example. Let’s say we have a task to generate a pre-defined target word based on only a collection of words that are built from 26 alphabet letters in lowercase. For instance, the target word is “big,” and we have a collection that consists of the words “sea,” “pig,” “...