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 Gradient Boosted Trees

Bayesian Optimization Gradient Boosted Trees (BOGBRT) is another variant of Bayesian Optimization that utilizes Gradient Boosted Trees as a surrogate model. Note that there will be endless variants of Bayesian Optimization that we can implement in skopt since we can just pass any other regressors from sklearn to be utilized as the base_estimator parameter. However, GBRT is part of the default surrogate model with predefined default hyperparameter values from the skopt package.

Similar to the Implementing Bayesian Optimization Random Forest section, we can just change the base_estimator parameter within optimizer_kwargs to GBRT. The following code shows you how to implement BOGBRT in skopt:

from skopt import BayesSearchCV

Initiate the BayesSearchCV class:

clf = BayesSearchCV(pipe, hyperparameter_space, n_iter=50,