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Louis Owen - Hyperparameter Tuning with Python

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Louis Owen Hyperparameter Tuning with Python
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Hyperparameter Tuning with Python Boost your machine learning models - photo 1
Hyperparameter Tuning with Python

Boost your machine learning models performance via hyperparameter tuning

Louis Owen

BIRMINGHAMMUMBAI Hyperparameter Tuning with Python Copyright 2022 Packt - photo 2

BIRMINGHAMMUMBAI

Hyperparameter Tuning with Python

Copyright 2022 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

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Publishing Product Manager: Dinesh Chaudhary

Senior Editor: David Sugarman

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First published: July 2022

Production reference: 1280722

Published by Packt Publishing Ltd.

Livery Place

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Birmingham

B3 2PB, UK.

ISBN 978-1-80323-587-5

www.packt.com

To Mom and Dad, thanks for everything!

Louis

Contributors
About the author

Louis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects. Finally, Louis loves to meet new friends! So, please feel free to reach out to him on LinkedIn if you have any topics to be discussed.

About the reviewer

Jamshaid Sohail is passionate about data science, machine learning, computer vision, and natural language processing and has more than 2 years of experience in the industry. He has worked at a Silicon Valley-based start-up named FunnelBeam, the founders of which are from Stanford University, as a data scientist. Currently, he is working as a data scientist at Systems Limited. He has completed over 66 online courses from different platforms. He authored the book Data Wrangling with Python 3.X for Packt Publishing and has reviewed multiple books and courses. He is also developing a comprehensive course on data science at Educative and is in the process of writing books for multiple publishers.

Table of Contents

Table of Contents

Table of Contents

Preface

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.

We will start the book with an introduction to hyperparameter tuning and explain why its important. Youll learn the best methods for hyperparameter tuning for a variety of use cases and a specific algorithm type. The book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are dedicated to giving full attention to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization.

Later in the book, you will learn about top frameworks such as scikit-learn, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, we will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameters.

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