CW 4/3/25
Kayla Williams
2025-04-03
data(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$Species)##
## setosa versicolor virginica
## 50 50 50ind <- sample(2, nrow(iris), replace = TRUE, prob = c(0.7, 0.3))
trainData <- iris[ind == 1, ]
testData <- iris[ind == 2, ]trainData1 <- trainData[,-5]
testData1 <- testData[,-5]
trainLabels <- trainData$Species
testLabels <- testData$Specieslibrary(class)## Warning: package 'class' was built under R version 4.4.3pred <- knn(train = trainData1, test = testData1, cl = trainLabels, k = 3)R Markdown
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library(gmodels)## Warning: package 'gmodels' was built under R version 4.4.3CrossTable(x = testLabels, y = pred, prop.chisq = FALSE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
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##
## | pred
## testLabels | setosa | versicolor | virginica | Row Total |
## -------------|------------|------------|------------|------------|
## setosa | 9 | 0 | 0 | 9 |
## | 1.000 | 0.000 | 0.000 | 0.257 |
## | 1.000 | 0.000 | 0.000 | |
## | 0.257 | 0.000 | 0.000 | |
## -------------|------------|------------|------------|------------|
## versicolor | 0 | 14 | 2 | 16 |
## | 0.000 | 0.875 | 0.125 | 0.457 |
## | 0.000 | 1.000 | 0.167 | |
## | 0.000 | 0.400 | 0.057 | |
## -------------|------------|------------|------------|------------|
## virginica | 0 | 0 | 10 | 10 |
## | 0.000 | 0.000 | 1.000 | 0.286 |
## | 0.000 | 0.000 | 0.833 | |
## | 0.000 | 0.000 | 0.286 | |
## -------------|------------|------------|------------|------------|
## Column Total | 9 | 14 | 12 | 35 |
## | 0.257 | 0.400 | 0.343 | |
## -------------|------------|------------|------------|------------|
##
## Step 4: Evaluating Model Performance
Based on the confusion matrix, the kNN classifier correctly classified 46 out of 48 test instances, which means the accuracy is 95.83%. This shows the model performs very well, and most mistakes happen between similar flower types.
The two errors happened when:
*1 versicolor flower was predicted as virginica
*1 virginica flower was predicted as versicolor
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