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 it’s important. You’ll 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.

By the end of the 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.