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 simulated annealing

SA is the heuristic search method that is inspired by the process of metal annealing in metallurgy. This method is similar to the random search hyperparameter tuning method (see Chapter 3, Exploring Exhaustive Search), except for the existence of a criterion that guides how the hyperparameter tuning process works. In other words, SA is like a smoothed version of random search. Just like random search, it is suggested to use SA when each trial doesn’t take too much time and you have enough computational resources.

In the metal annealing process, the metal is heated to a very high temperature for a certain time and slowly cooled to increase its strength, reducing its hardness and making it easier to work with. The goal of giving a very high heat is to excite the metal’s atoms so that they can move around freely and randomly. During this random movement, atoms usually tend to form a better configuration. Then, the slow cooling process...