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 manual search

Manual search is the most straightforward hyperparameter-tuning method that belongs to the exhaustive search group. In fact, it's not even an algorithm! There's no clear rule on how to perform this method. As its name would suggest, a manual search is performed based on your instinct. You simply have to tweak the hyperparameters until you are satisfied enough with the result.

This method is the one exception mentioned before in the introduction of this chapter. Except for this method, other methods in the exhaustive search group are categorized as uninformed search methods. You may already know the reason why this method is the exception. It's because the developer themselves learn what is the impact of changing a particular or a set of hyperparameters in each iteration. In other words, they learn from previous iterations to have a (hopefully) better "hyperparameter space" in the next iterations.

To perform a manual search...