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

Exploring SVM hyperparameters

Support Vector Machine (SVM) is an ML model that utilizes lines or hyperplanes, along with some linear algebra transformations, to perform a classification or regression task. All the algorithms discussed in the previous sections can be classified as tree-based algorithms, while SVM is not part of the tree-based group of ML algorithms. It is part of the distance-based group of algorithms. We usually called the linear algebra transformation in SVM a kernel. This is responsible for transforming any problem into a linear problem.

The most popular and well-maintained implementation of SVM in Python can be found in the scikit-learn package. It includes implementations for both regression (SVR) and classification (SVC) tasks. Both of them have very similar hyperparameters with only a few small differences. The following are the most important hyperparameters for SVM, starting with the most important to the least based on their impact on model performance...