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 Random Search

Implementing Random Search (see Chapter 3, Exploring Exhaustive Search) in sklearn is very similar to implementing Grid Search. The main difference is that we have to provide the number of trials or iterations since Random Search will not try all of the possible combinations in the hyperparameter space. Additionally, we have to provide the accompanying distribution for each of the hyperparameters when defining the search space. In sklearn, Random Search is implemented in the RandomizedSearchCV class.

To understand how we can implement Random Search in sklearn, let’s use the same example from the Implementing Grid Search section. Let’s directly try using all of the features available in the dataset. All of the pipeline creation processes are exactly the same, so we will directly jump into the process of how to define the hyperparameter space and the RandomizedSearchCV class. The following code shows you how to define the accompanying distribution...