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)
1
Section 1:The Methods
8
Section 2:The Implementation
13
Section 3:Putting Things into Practice

Introducing Hyperopt

All of the implemented optimization methods in the Hyperopt package assume we are working with a minimization problem. If your objective function is categorized as a maximization problem, for example, when you are using accuracy as the objective function score, you must add a negative sign to your objective function.

Utilizing the Hyperopt package to perform hyperparameter tuning is very simple. The following steps show how to perform any hyperparameter tuning methods provided in the Hyperopt package. More detailed steps, including the code implementation, will be given through various examples in the upcoming sections:

  1. Define the objective function to be minimized.
  2. Define the hyperparameter space.
  3. (Optional) Initiate the Trials() object and pass it to the fmin() function.
  4. Perform hyperparameter tuning by calling the fmin() function.
  5. Train the model on full training data using the best set of hyperparameters that have been found from...