CW040325

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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

library(class)
library(gmodels)

## Warning: package 'gmodels' was built under R version 4.4.3

iris <- datasets::iris
summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

set.seed(123)
ind <- sample(2, nrow(iris),replace = TRUE, prob =c(0.7,0.3))
trainData <- iris[ind == 1, ]
testData <- iris[ind == 2, ]

trainLabels <- trainData$Species
testLabels <- testData$Species

trainData1 <- trainData[, -5]
testData1 <- testData[, -5]

normalize <- function(x) {
  (x - min(x)) / (max(x) - min(x))
}

trainDataNorm <- as.data.frame(lapply(trainData1, normalize))
testDataNorm <- as.data.frame(lapply(testData1,normalize))

k_value <- round(sqrt(nrow(trainDataNorm)))
predictedLabels <- knn(train = trainDataNorm, test = testDataNorm, cl = trainLabels, k = k_value)

CrossTable(x = testLabels, y = predictedLabels, prop.chisq = FALSE)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  44 
## 
##  
##              | predictedLabels 
##   testLabels |     setosa | versicolor |  virginica |  Row Total | 
## -------------|------------|------------|------------|------------|
##       setosa |         15 |          0 |          0 |         15 | 
##              |      1.000 |      0.000 |      0.000 |      0.341 | 
##              |      1.000 |      0.000 |      0.000 |            | 
##              |      0.341 |      0.000 |      0.000 |            | 
## -------------|------------|------------|------------|------------|
##   versicolor |          0 |         12 |          2 |         14 | 
##              |      0.000 |      0.857 |      0.143 |      0.318 | 
##              |      0.000 |      0.857 |      0.133 |            | 
##              |      0.000 |      0.273 |      0.045 |            | 
## -------------|------------|------------|------------|------------|
##    virginica |          0 |          2 |         13 |         15 | 
##              |      0.000 |      0.133 |      0.867 |      0.341 | 
##              |      0.000 |      0.143 |      0.867 |            | 
##              |      0.000 |      0.045 |      0.295 |            | 
## -------------|------------|------------|------------|------------|
## Column Total |         15 |         14 |         15 |         44 | 
##              |      0.341 |      0.318 |      0.341 |            | 
## -------------|------------|------------|------------|------------|
## 
## 

accuracy <- sum(predictedLabels == testLabels) / length(testLabels)
print(paste("accuracy:",accuracy))

## [1] "accuracy: 0.909090909090909"

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