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

Introducing BO

BO is categorized as an informed search hyperparameter tuning method, meaning the search is learning from previous iterations to have a (hopefully) better subspace in the next iterations. It is also categorized as the sequential model-based optimization (SMBO) group. All SMBO methods work by sequentially updating probability models to estimate the effect of a set of hyperparameters on their performance based on historical observed data, as well as suggesting new hyperparameters to be tested in the following trials.

BO is a popular hyperparameter tuning method due to its data-efficient property, meaning it needs a relatively small number of samples to get to the optimal solution. You may be wondering, how exactly does BO get this ground-breaking data-efficient property? This property exists thanks to BO’s ability to learn from previous iterations. BO can learn and predict which subspace is worth visiting in the future by utilizing a probabilistic regression...