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

Exploring scikit-optimize

You were introduced to the Scikit-Optimize package in Chapter 7, Hyperparameter Tuning via Scikit, to conduct a hyperparameter tuning experiment. In this section, we will learn how to utilize this package to track all hyperparameter tuning experiments conducted using this package.

Scikit-Optimize provides very nice visualization plots that summarize the tested hyperparameter values, the objective function scores, and the relationship between them. Three plots are available in this package, as shown here. Please see the full code in this book’s GitHub repository for more details. The following plots were generated based on the same experimental setup that was provided in Chapter 7, Hyperparameter Tuning via Scikit, for the BOGP hyperparameter tuning method:

  • plot_convergence: This is used to visualize the hyperparameter tuning optimization progress for each iteration:

Figure 13.7 – Convergence plot