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 1 – using HTDM with a CatBoost classifier

Let’s say we are training a classifier based on the marketing campaign data that was introduced in Chapter 7, Hyperparameter Tuning via scikit. Here, we are utilizing CatBoost (see Chapter 11, Understanding Hyperparameters of Popular Algorithms) as the classifier. This is our first time working with the given data. The laptop we are using only has a single-core CPU and the hyperparameter space is defined as follows. Note that we are not working with a conditional hyperparameter space:

  • iterations: randint(5,200)
  • depth: randint(3,10)
  • learning_rate: np.linspace(1e-5,1e-3,20)
  • l2_leaf_reg: np.linspace(1,30,30)
  • one_hot_max_size: randint(2,15)

Based on the given case description, we can try to utilize HTDM to help us choose which hyperparameter tuning suits the condition the best. First of all, we know that we do not have any prior knowledge or meta-learning results of the good hyperparameter...