!!! Overview
[{$pagename}] analysis is a subcategory of [Supervised Learning] where the [Predictor variable] is the [Classification] to which the [data] belongs.

The simplest [{$pagename}] is a [Binary] [{$pagename}] as in:
* Cat
* NOT Cat

[Classification] and [{$pagename}] is a subcategory of [Supervised Learning] where the goal is to predict the [Classification] labels of new instances, based on past observations. Those [Classification] labels are discrete, unordered values that can be understood as the [group] memberships of the instances. 

The set of [Classification] labels does not have to be of a [binary] nature. The [Mapping function] learned by a [Supervised Learning] [algorithm] can assign any [Classification] label that was presented in the [training dataset] to a new, unlabeled instance. 

A typical example of a multiclass [Classification] task is handwritten character recognition. Where the [Training dataset] that consists of multiple handwritten examples of each letter in the alphabet. However, our [Machine Learning model] would be unable to correctly recognize any of the digits zero to nine, for example, if they were not part of our [training dataset].

!! More Information
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