MLLabelUtils.jl’s documentation

This package represents a community effort to provide the necessary functionality for interpreting class-predictions, as well as converting classification targets from one encoding to another. As such it is part of the JuliaML ecosystem.

The main intend of this package is to be a light-weight back-end for other JuliaML packages that deal with classification problems. In particular, this library is designed with package developers in mind that require their classification-targets to be in a specific format. To that end, the core focus of this package is to provide all the tools needed to deal with classification targets of arbitrary format. This includes asserting if the targets are of a desired encoding, inferring the concrete encoding the targets are in and how many classes they represent, and converting from their native encoding to the desired one.

From an end-user’s perspective one normally does not need to import this package directly. That said, some functionality (in particular convertlabels()) can also be useful to end-users who code their own special Machine Learning scripts.

Where to begin?

If this is the first time you consider using MLLabelUtils for your machine learning related experiments or packages, make sure to check out the “Getting Started” section; specifically “How to ...?”, which lists some of most common scenarios and links to the appropriate places that should guide you on how to approach these scenarios using the functionality provided by this or other packages.

API Documentation

This section gives a more detailed treatment of all the exposed functions and their available methods. We start by discussing what we understand under terms such as “classification targets” and the available functionality to compute properties about them.

Next we focus on label-encodings. We will show how to create them and how they can be used to transform classification targets from one encoding-convention to another. Some even define methods for a classification function that can be used to transform raw mode-predictions into a class-label.

Lastly, we provide an organized list of the implemented label-encoding that this package exposes. We will also discuss their properties and differences or other nuances.

Indices and tables