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

Introducing DEAP

Distributed Evolutionary Algorithms in Python (DEAP) is a Python package that allows you to implement various evolutionary algorithms including (but not limited to) the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). To install DEAP, you can simply call the pip install deap command.

DEAP allows you to craft your evolutionary algorithm optimization steps in a very flexible manner. The following steps show how to utilize DEAP to perform any hyperparameter tuning methods. More detailed steps, including the code implementation, will be given through various examples in the upcoming sections:

  1. Define the type classes through the creator.create() module. These classes are responsible for defining the type of objects that will be used in the optimization steps.
  2. Define the initializers along with the hyperparameter space and register them in the base.Toolbox() container. The initializers are responsible for setting the initial value of the objects...