Introduction
Binary Classification is a type of machine learning wherein a learning algorithm is tasked with classifying features with a binary ‘true’ or ‘false’ label.
LunchBoxML implements Microsoft’s ML.NET binary classification with a variety of learning algorithm types to select from and calibrate.
Simple Classification Region
This example file uses a Binary Classification Trainer based on random points and their inclusion within a 2D enclosed region.
A Binary Classification tester is used to predict whether a new point is contained within the region.
Multi-label Classifier using Binary Components
This example shows how users can employ several Binary Classifiers to set up a Multi-Label Classification scenario.
The example trains three binary classification trainers using 3D point collection and their inclusion within three regions.
A new point grid is then tested against the binary classifiers. A point has the possibility being assigned 0-3 labels depending on their inclusion within each of the regions.
