# R - K-Nearest Neighbors (KNN) Analysis

### Table of Contents

## 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.