Hyperparameter Tuning with Python: Boost your machine learning model’s performance via hyperparameter tuning PDF
Preview Hyperparameter Tuning with Python: Boost your machine learning model’s performance via hyperparameter tuning
Download Packt Books Free https://t.me/prog_books_Packt Hyperparameter Tuning with Python Boost your machine learning model’s performance via hyperparameter tuning Louis Owen BIRMINGHAM—MUMBAI Download Packt Books Free https://t.me/prog_books_Packt 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. Group Product Manager: Gebin George Publishing Product Manager: Dinesh Chaudhary Senior Editor: David Sugarman Technical Editor: Devanshi Ayare Copy Editor: Safis Editing Project Coordinator: Farheen Fathima Proofreader: Safis Editing Indexer: Pratik Shirodkhar Production Designer: Ponraj Dhandapani Marketing Coordinator: Shifa Ansari and Abeer Riyaz Dawe First published: July 2022 Production reference: 1280722 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-80323-587-5 www.packt.com To Mom and Dad, thanks for everything! – Louis Download Packt Books Free https://t.me/prog_books_Packt Co n t r i b u t o r s 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. Download Packt Books Free https://t.me/prog_books_Packt Table of Contents Preface Section 1: The Methods 1 Evaluating Machine Learning Models Technical requirements 4 Discovering Leave-One-Out Understanding the concept cross-validation 12 of overfitting 4 Discovering LPO cross-validation 13 Creating training, validation, Discovering time-series and test sets 5 cross-validation 14 Exploring random and stratified splits 6 Summary 16 Discovering repeated k-fold Further reading 16 cross-validation 11 2 Introducing Hyperparameter Tuning What is hyperparameter tuning? 17 Understanding hyperparameter Demystifying hyperparameters space and distributions 19 versus parameters 18 Summary 20 viii Table of Contents 3 Exploring Exhaustive Search Understanding manual search 22 Understanding random search 25 Understanding grid search 23 Summary 28 4 Exploring Bayesian Optimization Introducing BO 30 Understanding TPE 51 Understanding BO GP 40 Understanding Metis 55 Understanding SMAC 42 Summary 58 5 Exploring Heuristic Search Understanding simulated annealing 60 Understanding Population-Based Understanding genetic algorithms 65 Training 82 Understanding particle swarm Summary 86 optimization 74 6 Exploring Multi-Fidelity Optimization Introducing MFO 88 Understanding hyper band 100 Understanding coarse-to-fine search 89 Understanding BOHB 103 Understanding successive halving 95 Summary 107 Table of Contents ix Section 2: The Implementation 7 Hyperparameter Tuning via Scikit Technical requirements 112 Implementing Bayesian Introducing Scikit 112 Optimization Gaussian Process 130 Implementing Grid Search 114 Implementing Bayesian Optimization Random Forest 133 Implementing Random Search 121 Implementing Bayesian Implementing Coarse-to-Fine Search 123 Optimization Gradient Boosted Trees 134 Implementing Successive Halving 124 Summary 136 Implementing Hyper Band 128 8 Hyperparameter Tuning via Hyperopt Technical requirements 138 Implementing Tree-structured Introducing Hyperopt 138 Parzen Estimators 146 Implementing Random Search 141 Implementing Adaptive TPE 146 Implementing simulated annealing 148 Summary 150 9 Hyperparameter Tuning via Optuna Technical requirements 152 Implementing Simulated Annealing 162 Introducing Optuna 152 Implementing Successive Halving 164 Implementing TPE 159 Implementing Hyperband 166 Implementing Random Search 160 Summary 167 Implementing Grid Search 161
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