K-Nearest Neighbour

Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How to use K-Nearest Neighbour in Machine Learning model

Objectives

• Learn how to use KNN in ML model

13 K-Nearest Neighbour

• Simplicity but powerful and fast for certain task
• Work for both classification and regression
• Named as Instance Based Learning; Non-parametrics; Lazy learner
• Work well with small number of inputs

13.1 Explanation

• In KNN, the most important parameter is the K group and the distance computed between points.
• Euclide distance:

13.2 Implementation

library(caret)
data(iris)
set.seed(123)
indT <- createDataPartition(y=iris$Species,p=0.6,list=FALSE)
training <- iris[indT,]
testing  <- iris[-indT,]

ModFit_KNN <- train(Species~.,training,method="knn",preProc=c("center","scale"),tuneLength=20)

ggplot(ModFit_KNN$results,aes(k,AccuracySD))+
      geom_point(color="blue")+
      labs(title=paste("Optimum K is ",ModFit_KNN$bestTune),
           y="Error")
      
predict_KNN<- predict(ModFit_KNN,newdata=testing)
confusionMatrix(testing$Species,predict_KNN)

Key Points

• KNN