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

Demystifying hyperparameters versus parameters

The key difference between a hyperparameter and a parameter is how its value is generated. A parameter value is generated by the model during the model-training phase. In other words, its value is learned from the given data instead of given by the developer. On the other hand, a hyperparameter value is given by the developer since it can't be estimated from the data.

Parameters are like the heart of the model. Poorly estimated parameters will result in a poorly performing model. In fact, when we said we are training a model, it actually means that we are providing the data to the model so that the model can estimate the value of its parameters, which is usually done by performing some kind of optimization algorithm. Here are several examples of parameters in ML:

  • Coefficients () in linear regression
  • Weights () in a multilayer perceptron (MLP)

Hyperparameters, on the other hand, are a set of values that support...