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 Scikit

scikit-learn, which is commonly called Sklearn, is a very popular open source package in Python that is widely used for ML-related tasks, starting from data preprocessing, model training and evaluation, model selection, hyperparameter tuning, and more. One of the main selling points of the sklearn package is the consistency of its interface across many implemented classes.

For example, all of the implemented ML models, or estimators, in sklearn have the same fit() and predict() methods for fitting the model on the training data and evaluating the fitted model on the test data, respectively. When working with data preprocessors, or transformers, in sklearn, the typical method that every preprocessor has is the fit(), transform(), and fit_transform() methods for fitting the preprocessor, transforming new data with the fitted preprocessor, and fitting and directly transforming the data that is used to fit the preprocessor, respectively.

In Chapter 1, Evaluating...