# K-Nearest-Neighbors and Handwritten Digit Classification

DevFeed: [K-Nearest-Neighbors and Handwritten Digit Classification](<https://devfeed.tech/articles/k-nearest-neighbors-and-handwritten-digit-classification-40284.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2012/08/26/k-nearest-neighbors-and-handwritten-digit-classification/>)

Published: 2012-08-26T12:19:43Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [Math and Logic](<https://devfeed.tech/topics/math-and-logic.md>)

Tags: [dimension](<https://devfeed.tech/tags/dimension.md>), [linear-algebra](<https://devfeed.tech/tags/linear-algebra.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [metric](<https://devfeed.tech/tags/metric.md>), [process](<https://devfeed.tech/tags/process.md>), [programming](<https://devfeed.tech/tags/programming.md>), [supervised-learning](<https://devfeed.tech/tags/supervised-learning.md>)

## AI overview

This tutorial introduces supervised classification in machine learning and explains how labeled data, training algorithms, models, and classification algorithms work together. It then examines the assumption that data points in the same class are close under an appropriate metric.

## Source excerpt

The Recipe for Classification One important task in machine learning is to classify data into one of a fixed number of classes. For instance, one might want to discriminate between useful email and unsolicited spam. Or one might wish to determine the species of a beetle based on its physical attributes, such as weight, color, and mandible length. These "attributes" are often called "features" in the world of machine learning, and they often correspond to dimensions when interpreted in the framework of linear algebra.