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

Understanding coarse-to-fine search

Coarse-to-Fine Search (CFS) is a combination of grid and random search hyperparameter tuning methods (see Chapter 3, Exploring Exhaustive Search). Unlike grid and random search, which are categorized in the uninformed search group of methods, CFS utilizes knowledge from previous iterations to have a (hopefully) better search space in the future. In other words, CFS is a combination of sequential and parallel hyperparameter tuning methods. It is indeed a very simple method since it is basically a combination of two other simple methods: grid and random search.

CFS can be effectively utilized as a hyperparameter tuning method when you are working with a medium-sized model, for example, a shallow neural network (other types of models can also work) and a moderate amount of training data.

The main idea of CFS is just to start with a coarse random search from the whole hyperparameter space, then gradually refine the search in more detail, either...