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

Getting familiar with HTDM

HTDM is designed to help you decide which hyperparameter tuning method should be adopted in a particular situation (see Figure 12.1). Here, the situation is defined based on six aspects:

  • Hyperparameter space properties, including the size of the space, types of hyperparameter values (numerical only or mixed), and whether it contains conditional hyperparameters or not
  • Objective function complexity: whether it is a cheap or expensive objective function
  • Computational resource availability: whether or not you have enough parallel computational resources
  • Training data size: whether you have a few, moderate, or a large number of training samples
  • Prior knowledge availability: whether you have prior knowledge of the good range of hyperparameter values
  • Types of ML algorithms: whether you are working with a small, medium, or large-sized model, and whether you are working with a traditional ML or deep learning type of algorithm