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 Hyper Band

The extension of Successive Halving, the Hyper Band (HB) method (see Chapter 6), is implemented in the scikit-hyperband package. This package is built on top of sklearn, which means it also provides a very similar interface for GridSearchCV, RandomizedSearchCV, HalvingGridSearchCV, and HalvingRandomSearchCV.

In contrast with the default SH budget definition in the sklearn implementation, Scikit-Hyperband defines the budget as the number of estimators, n_estimators, in an ensemble of trees, or the number of iterations for estimators trained with stochastic gradient descent, such as the XGBoost algorithm. Additionally, we can use any other hyperparameters that exist in the estimator as the budget definition. However, scikit-hyperband doesn’t allow us to use the number of samples as the budget definition.

Let’s use the same example as in the Implementing Successive Halving section, but with a different hyperparameter space. Here, we use the...