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


In this chapter, we have learned all the important things about the DEAP and Microsoft NNI packages. We also have learned how to implement various hyperparameter tuning methods with the help of these packages, along with understanding each of the important parameters of the classes and how are they related to the theory that we have learned in the previous chapters. From now on, you should be able to utilize these packages to implement your chosen hyperparameter tuning method, and ultimately, boost the performance of your ML model. Equipped with the knowledge from Chapters 3 – 6, you will also be able to debug your code if there are errors or unexpected results, and be able to craft your own experiment configuration to match your specific problem.

In the next chapter, we’ll learn about hyperparameters for several popular algorithms. There will be a wide explanation for each of the algorithms, including (but not limited to) the definition of each hyperparameter...