Overview#

Classification (or Taxonomy) is the problem of Identification of an Entity to determine a set of categories (sub-populations) a new observation belongs.

Classification in the simplest form is a binary Classification as in "CAT" or "NOT a CAT".

Classification Machine Learning#

Classification in Machine Learning and statistics is the problem of Identification of an Entity to determine a set of categories (sub-populations) a new observation belongs, on the basis of a training dataset of data containing observations (or instances) whose Classification membership is known.

Classification is an example of Pattern-recognition.

Classification in the terminology of Machine Learning is considered an instance of Supervised Learning, i.e. learning where a training dataset of correctly identified observations is available. The corresponding unsupervised Learning procedure is known as cluster analysis, and involves grouping data into categories based on some measure of inherent similarity or distance.

Data Classification#

Data Classification may be more specific.

An algorithm that implements Classification, especially in a concrete implementation, is known as a classifier.

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