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 13: Tracking Hyperparameter Tuning Experiments

Working with a lot of experiments can sometimes be overwhelming. Many iterations of experiments will need to be done. It will become even more complicated when we are experimenting with many ML models.

In this chapter, you will be introduced to the importance of tracking hyperparameter tuning experiments, along with the usual practices. You will also be introduced to several open source packages that are available and learn how to utilize each of them in practice.

By the end of this chapter, you will be able to utilize your favorite package to track your hyperparameter tuning experiment. Being able to track your hyperparameter tuning experiment will boost the effectiveness of your workflow.

In this chapter, we will cover the following topics:

  • Revisiting the usual practices
  • Exploring Neptune
  • Exploring Scikit-Optimize
  • Exploring Optuna
  • Exploring Microsoft NNI
  • Exploring MLflow