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Classification Trees
Classification Trees

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!!! 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
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
This page (revision-1) was last changed on 29-Nov-2024 16:16 by UnknownAuthorTop