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 SMAC

SMAC is part of the BO hyperparameter tuning method group and utilizes random forest as the surrogate model. This method is optimized to handle discrete or categorical hyperparameters. If your hyperparameter space is huge and is dominated by discrete hyperparameters, then SMAC is a good choice for you.

Similar to BOGP, SMAC also works by modeling the objective function. Specifically, it utilizes random forest as the surrogate model to create an estimation of the real objective function, which can then be passed to the acquisition function (see the Introducing BO section for more details).

Random forest is a machine learning (ML) algorithm that can be utilized in classification or regression tasks. It is built upon a collection of decision trees, which is known to perform well with categorical types of features. The name random forest comes from the fact that it is built from several decision trees. We will discuss random forest, along with its hyperparameters...