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)
1
Section 1:The Methods
8
Section 2:The Implementation
13
Section 3:Putting Things into Practice

Implementing Successive Halving

Similar to CFS, Successive Halving (SH) is also part of the Multi-Fidelity Optimization group (see Chapter 6). There are two implementations of SH in sklearn, namely HalvingGridSearchCV and HalvingRandomSearchCV. As their names suggest, the former class is an implementation of SH that utilizes Grid Search in each of the SH iterations, while the latter utilizes Random Search.

By default, SH implementations in sklearn use the number of samples, or n_samples, as the definition of the budget or resource in SH. However, it is also possible to define a budget with other definitions. For example, we can use n_estimators in RF as the budget, instead of using the number of samples. It is worth noting that we cannot use n_estimators, or any other hyperparameters, to define the budget if it is part of the hyperparameter space.

Both HalvingGridSearchCV and HalvingRandomSearchCV have similar standard SH parameters to control how the SH iterations will work...