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

Introducing Optuna

Optuna is a hyperparameter tuning package in Python that provides several hyperparameter tuning methods implementation, such as Grid Search, Random Search, Tree-structured Parzen Estimators (TPE), and many more. Unlike Hyperopt, which assumes we are always working with a minimization problem (see Chapter 8, Hyperparameter Tuning via Hyperopt), we can tell Optuna the type of optimization problem we are working on: minimization or maximization.

Optuna has two main classes, namely samplers and pruners. Samplers are responsible for performing the hyperparameter tuning optimization, whereas pruners are responsible for judging whether we should prune the trials based on the reported values. In other words, pruners act like early stopping methods where we will stop a hyperparameter tuning iteration whenever it seems that there’s no additional benefit to continuing the process.

The built-in implementation for samplers includes several hyperparameter tuning...