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 grid search

Grid search is the simplest automated hyperparameter-tuning method that ever existed. Apart from the fancy name, grid search is basically just a nested for loop that tests all possible hyperparameter values in the search space. Although many packages have grid search as one of their hyperparameter-tuning method implementations, it is super easy to write your own code from scratch to implement this method. The name grid comes from the fact that we have to test the whole hyperparameter space just like creating a grid, as illustrated in the following diagram.

Figure 3.2 – Grid search illustration

For example, let's say we want to perform hyperparameter tuning using the grid search method on a random forest. We decide to focus only on the number of estimators, splitting criterion, and maximum tree-depth hyperparameters. Then, we can specify a list of possible values for each of the hyperparameters. Let's say we define...