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

Chapter 7: Hyperparameter Tuning via Scikit

scikit-learn is one of the Python packages that is used the most by data scientists. This package provides a range of Machine Learning (ML)-related modules that are ready to be used with minimum effort, including for the task of hyperparameter tuning. One of the main selling points of this package is its consistent interface across many implemented classes, which almost every data scientist loves! Apart from scikit-learn, there are also other packages for the hyperparameter tuning task that are built on top of scikit-learn or mimic the interface of scikit-learn, such as scikit-optimize and scikit-hyperband.

In this chapter, we’ll learn about all of the important things to do with scikit-learn, scikit-optimize, and scikit-hyperband, along with how to utilize them to implement the hyperparameter tuning methods that we learned about in the previous chapters. We’ll start by walking through how to install each of the packages...