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 12: Introducing Hyperparameter Tuning Decision Map

Getting too much information can sometimes lead to confusion, which can, in turn, lead back to adopting the simplest option. We learned about numerous hyperparameter tuning methods in the previous chapters. Although we have discussed the ins and outs of each method, it will be very useful for us to have a single source of truth that can be used to help us decide which method to use in which situation.

In this chapter, you’ll be introduced to the Hyperparameter Tuning Decision Map (HTDM), which summarizes all of the discussed hyperparameter tuning methods into a simple decision map based on many aspects, including the properties of the hyperparameter space, the complexity of the objective function, training data size, computational resources availability, prior knowledge availability, and the types of ML algorithms we are working with. There will be also three study cases that show how to utilize HTDM in practice.