Book Image

Machine Learning With Go

Book Image

Machine Learning With Go

Overview of this book

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.
Table of Contents (11 chapters)

Entropy, information gain, and related methods

In Chapter 5, Classification, we explored decision tree methods in which models consisted of a tree of if/then statements. These if/then portions of the decision tree split the prediction logic based on one of the features of the training set. In an example where we were trying to classify medical patients into unhealthy or healthy categories, a decision tree might first split based on a gender feature, then based on an age feature, then based on a weight feature, and so on, eventually landing on healthy or unhealthy.

How does the algorithm choose which features to use first in the decision tree? In the preceding example, we could split on gender first, or weight first, and any other feature first. We need a way to arrange our splits in an optimal way, such that our model makes the best predictions that it can make.

Many decision...