Classification

The Classification panel allows to test, tweak and observe how different algorithms perform classification on samples living in a N-dimensional space: "the canvas".
Classification can be Binary or Multi-Class, depending on whether there are presently more than 2 classes of samples (different colors) and whether the algorithm allows it.

The canvas will display the results of the classification in multiple layers, which can be changed using the display options. These are:
In the case of binary classification, the red color is used to indicate the positive class (by default class #1) while white color indicates the negative class. Varying degrees of blackness indicate uncertainty (for algorithms that do not have harsh class transitions)

In Practice
The easiest way to perform classification is to:
  1. Draw some samples (left-click: class 1, right-click: class 0)
  2. Click on "Classify"
This should train the algorithm and start painting the canvas with the results of the classification.

Options and Commands
The interface for classification (the right-hand side of the Algorithm Options dialog) provides the following commands:
and the following options:
All other options are algorithm-dependent and should be described in the help menu of the algorithm itself.