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

Chapter 6: Exploring Multi-Fidelity Optimization

Multi-Fidelity Optimization (MFO) is the fourth of four groups of hyperparameter tuning methods. The main characteristic of this group is that all methods belonging to this group utilize the cheap approximation of the whole hyperparameter tuning pipeline so we can have similar performance results with a much lower computational cost and faster experiment time. This group is suitable when you have a very large model or a very large number of samples, for example, when you are developing a neural-network-based model.

In this chapter, we will discuss several methods in the MFO group, including coarse-to-fine search, successive halving, hyper band, and Bayesian Optimization and Hyperband (BOHB). As in Chapter 5, Exploring Heuristic Search we will discuss the definition of each method, the differences between them, how they work, and the pros and cons of each.

By the end of this chapter, you will be confident in explaining MFO and its...