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

Case study 3 – using HTDM with prior knowledge of the hyperparameter values

Let’s say, in this case, we are also faced with a similar condition as in the previous case study, but this time, we have prior knowledge of the good hyperparameter values for the given data since one of the data scientists in our team has worked with the same data previously. This means we will only focus on the left branch of the first node in HTDM, as shown here:

Figure 12.10 – Case study 3, have prior knowledge

Based on the given case description, we know that we do not have enough parallel computational resources since we only have a single-core CPU. This means we will only focus on the right branch of the second node, as shown here:

Figure 12.11 – Case study 3, not enough parallel computational resources

We also know that we have a medium-sized hyperparameter space that only consists of numerical types of values. This means...