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 Bayesian Optimization Random Forest

Bayesian Optimization Random Forest (BORF) is another variant of Bayesian Optimization hyperparameter tuning methods that utilize RF as the surrogate model. Note that this variant is different from Sequential Model Algorithm Configuration (SMAC) although both of them utilize RF as the surrogate model (see Chapter 4, Exploring Bayesian Optimization).

Implementing BORF with skopt is actually very similar to implementing BOGP as discussed in the previous section. We just need to change the base_estimator parameter within optimizer_kwargs to RF. Let’s use the same example as in the Implementing Bayesian Optimization Gaussian Process section, but change the acquisition function from EI to LCB. Additionally, let’s change the xi parameter in the acq_func_kwargs to kappa since we are using LCB as our acquisition function. Note that we can also still use the same acquisition function. The changes made here just to show how you...