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 5: Exploring Heuristic Search

Heuristic search is the third out of four groups of hyperparameter tuning methods. The key difference between this group and the other groups is that all the methods that belong to this group work by performing trial and error to achieve the optimal solution. Similar to the acquisition function in Bayesian optimization (see Chapter 4, Exploring Bayesian Optimization), all methods in this group also employ the concept of exploration versus exploitation. Exploration means performing a search in the unexplored space to lower the probability of being stuck in the local optima, while exploitation means performing a search in the local space that is known to have a good chance of containing the optimal solution.

In this chapter, we will discuss several methods that belong to the heuristic search group, including simulated annealing (SA), genetic algorithms (GAs), particle swarm optimization (PSO), and Population-Based Training (PBT). Similar to Chapter...