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

Implementing the Genetic Algorithm

GA is one of the variants of the Heuristic Search hyperparameter tuning group (see Chapter 5) that can be implemented by the DEAP package. To show you how we can implement GA with the DEAP package, let’s use the Random Forest classifier model and the same data as in the examples in Chapter 7. The dataset used in this example is the Banking Dataset – Marketing Targets dataset provided on Kaggle (

The target variable consists of two classes, yes or no, indicating whether the client of the bank has subscribed to a term deposit or not. Hence, the goal of training an ML model on this dataset is to identify whether a customer is potentially wanting to subscribe to the term deposit or not. Out of the 16 features provided in the data, there are seven numerical features and nine categorical features. As for the target class distribution, 12% of them are yes...