Classification learning

Classification learning is a type of machine learning that classifies entities based on their characteristics Classification learning works by giving a classification learning algorithm a set of training examples that have already been classified; the algorithm then produces a model that can be used to classify other, unclassified entities. Classification outputs predict a single, nominal attribute of each input entity. A nominal attribute is a label or classification that is mutually exclusive to the entity (it can have one and only one value).

A simple example of this would be to classifying whether a customer will buy a new mobile phone in the next year as a simple "yes" or "no" classification. The training data might look something like this (but with with thousands more records):

idgenderagerecently movedcredit ratingbuy a phone?

And the model produced could be a decision tree model, such as this:

graph TD Age{Age}-->| < 18 | No15((No)) Age-->| 18 - 65 | Credit18{Credit Rating} Credit18-->|Excellent| Yes18((Yes)) Credit18-->|Good| Yes18 Credit18-->|Fair| No18((No)) Age{Age}-->| > 65 | Moved65{Recently Moved} Moved65-->|Yes| Yes65((Yes)) Moved65-->|No| No65((No))

Deeper Knowledge on Classification Learning

1R Classification Algorithm

A simple classification machine learning algorithm

Broader Topics Related to Classification Learning

Data Mining

A guide to finding patterns and relationships in data

Machine Learning (ML)

Machine learning terms, processes, and methods

Classification Learning Knowledge Graph