R - K-Nearest Neighbors (KNN) Analysis

About

Steps

Prerequisites

library(class)

Syntax

?knn
knn(train, test, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)

where:

  • k is number of neighbours to be considered.
  • train is the training set
  • c1 is the factor of the training set with the true target
  • test is the test set

Training and Test Data set

  • The knn function is waiting for two matrix (a training set and a test set)
# To be able to call all data frame variables by names
attach(myDataFrame)

#  Make a matrix of the chosen variables variable1 and variable1
variables=cbind(variable1,variable2)

# Make an indicator (a vector of true or false)
indicator=variableName<10

# The training set will be then
variables[indicator,]
# And the test set will be:
variables[!indicator,]

Model

Call to the knn function to made a model

knnModel=knn(variables[indicator,],variables[!indicator,],target[indicator]],k=1)

To classify a new observation, knn goes into the training set in the x space, the feature space, and looks for the training observation that's closest to your test point in Euclidean distance and classify it to this class.

Accuracy

Confusion Matrix

table(knnModel,variables[!indicator])
knnModel  False True
    False    43   58
    True     68   83

Mean

mean(knnModel==variables[!indicator])
[1] 0.5

It was useless as One nearest neighbor did no better than flipping a coin.

Next

We could proceed further and try nearest neighbors with multiple values of k.


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