Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest details
Key Features
- Gain a deep understanding of how hyperparameter tuning works
- Explore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methods
- Learn which method should be used to solve a specific situation or problem
Book Description
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.
What you will learn
- Discover hyperparameter space and types of hyperparameter distributions
- Explore manual, grid, and random search, and the pros and cons of each
- Understand powerful underdog methods along with best practices
- Explore the hyperparameters of popular algorithms
- Discover how to tune hyperparameters in different frameworks and libraries
- Deep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAP
- Get to grips with best practices that you can apply to your machine learning models right away
Who this book is for
This book is for data scientists and ML engineers who are working with Python and want to further boost their ML model's performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.
Table of Contents
- Evaluating Machine Learning Models
- Introducing Hyperparameter Tuning
- Exploring Exhaustive Search
- Exploring Bayesian Optimization
- Exploring Heuristic Search
- Exploring Multi-Fidelity Optimization
- Hyperparameter Tuning via Scikit
- Hyperparameter Tuning via Hyperopt
- Hyperparameter Tuning via Optuna
- Advanced Hyperparameter Tuning with DEAP and Microsoft NNI
- Understanding Hyperparameters of Popular Algorithms
- Introducing Hyperparameter Tuning Decision Map
- Tracking Hyperparameter Tuning Experiments
- Conclusions and Next Steps
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.
