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 BO GP

Bayesian optimization Gaussian process (BOGP) is one of the variants of the BO hyperparameter tuning method. It is well-known for its good capability in describing the objective function. This variant is very popular due to the unique analytically tractable nature of the surrogate model and its ability to produce relatively accurate approximation, even with only a few observed points.

However, BOGP has limitations. It only works on continuous hyperparameters, not on the discrete or categorical types of hyperparameters. It is not recommended to use BOGP when you need a lot of iterations to get the optimal set of hyperparameters, especially when you have a large number of samples. This is BOGP has a runtime, where is the number of samples. If you have more than 10 hyperparameters to be optimized, the common belief is that BOGP is not the right hyperparameter tuning method for you.

Having GP as the surrogate model means that we utilize GP as the prior for...