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

Implementing Bayesian Optimization Gaussian Process

Bayesian Optimization Gaussian Process (BOGP) is one of the variants of the Bayesian Optimization hyperparameter tuning group (see Chapter 4, Exploring Bayesian Optimization). To implement BOGP, we can utilize the skopt package. Similar to scikit-hyperband, this package is also built on top of the sklearn package, which means the interface for the implemented Bayesian Optimization tuning class, BayesSearchCV, is very similar to GridSearchCV, RandomizedSearchCV, HalvingGridSearchCV, HalvingRandomSearchCV, and HyperbandSearchCV.

However, unlike sklearn or scikit-hyperband, which works well directly with the distribution implemented in scipy, in skopt, we can only use the wrapper provided by the package when defining the hyperparameter space. The wrappers are defined within the instances and consist of three types of dimensions, such as Real, Integer, and Categorical. Within each of these dimension wrappers...