> For the complete documentation index, see [llms.txt](https://lab-of-ontologies-language-proce.gitbook.io/ladsbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lab-of-ontologies-language-proce.gitbook.io/ladsbook/partiji-chu-zhi-shi/machine_learning/knn.md).

# kNN

\#### k-Nearest Neighbours (kNN)

\*\*k-nearest neighbors algorithm (k-NN)\*\* 可能是最簡單的機器學習演算法，但是在許多場合還是很好用。

* 不預設資料的分佈型態。
* 如果很難定義概念，但是知道資料何時出現時。
* 利用資料的「同類相聚」特徵，classifying unlabeled examples by assigning them the class of similar labeled examples.

\`\`\`

\# Import required library

library(knn)

\# Fitting model

fit <-knn(y\_train \~ ., data = x, k = 4)

summary(fit)

\# Predict Output

predicted <- predict(fit, x\_test)

\`\`\`