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 Random Search

To implement Random Search (see Chapter 3) in Hyperopt, we can simply follow the steps explained in the previous section and pass the rand.suggest object to the algo parameter in the fmin() function. Let’s learn how we can utilize the Hyperopt package to perform Random Search. We will use the same data and sklearn pipeline definition as in Chapter 7, Hyperparameter Tuning via Scikit, but with a slightly different definition of the hyperparameter space. Let’s follow the steps that were introduced in the previous section:

  1. Define the objective function to be minimized. Here, we are utilizing the defined pipeline, pipe, to calculate the 5-fold cross-validation score by utilizing the cross_val_score function from sklearn. We will use the F1 score as the evaluation metric:
    import numpy as np
    from sklearn.base import clone
    from sklearn.model_selection import cross_val_score
    from hyperopt import STATUS_OK
    def objective(space):