library(knitr)
library(class)
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.3
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## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## Warning: package 'ggplot2' was built under R version 4.1.3
## Warning: package 'tidyr' was built under R version 4.1.3
## Warning: package 'dplyr' was built under R version 4.1.3
## Warning: package 'stringr' was built under R version 4.1.3
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## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(GGally)
## Warning: package 'GGally' was built under R version 4.1.3
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
library(ggplot2)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
# Some minor changes in the data set
iris2 <- iris %>%
rename(`Sepal length`=Sepal.Length,
`Sepal width`=Sepal.Width,
`Petal length`=Petal.Length,
`Petal width`=Petal.Width) %>%
mutate(Species=fct_recode(Species, "Setosa"="setosa",
"Versicolor"="versicolor",
"Virginica"="virginica"))
# Density plot for each species
iris2 %>%
gather(Attributes, value, 1:4) %>%
ggplot(aes(x=value, fill=Species)) +
geom_density(colour="black", alpha=0.5) +
facet_wrap(~Attributes, scales="free_x") +
labs(x="Values", y="Density",
title="Iris data set",
subtitle="Density plot for each attribute") +
theme_bw() +
theme(legend.position="bottom",
legend.title=element_blank())
# Violin plot for each attribute
iris2 %>%
gather(Attributes, value, 1:4) %>%
ggplot(aes(x=reorder(Attributes, value, FUN=median), y=value, fill=Attributes)) +
geom_violin(show.legend=FALSE) +
geom_boxplot(width=0.05, fill="white") +
labs(title="Iris data set",
subtitle="Violin plot for each attribute") +
theme_bw() +
theme(axis.title.y=element_blank(),
axis.title.x=element_blank())
# Boxplot for each attribute
iris2 %>%
gather(Attributes, value, 1:4) %>%
ggplot(aes(x=reorder(Attributes, value, FUN=median), y=value, fill=Attributes)) +
geom_boxplot(show.legend=FALSE) +
labs(title="Iris data set",
subtitle="Boxplot for each attribute") +
theme_bw() +
theme(axis.title.y=element_blank(),
axis.title.x=element_blank())
# Scatter plot and correlations
ggpairs(cbind(iris2, Cluster=as.factor(iris2$Species)),
columns=1:4, aes(colour=Cluster, alpha=0.5),
lower=list(continuous="points"),
axisLabels="none", switch="both") +
theme_bw()
# Normalization of all columns except Species
dataNorm <- iris
dataNorm[, -5] <- scale(iris[, -5])
# Reproducible results
set.seed(1234)
# 70% train and 30% test
ind <- sample(2, nrow(dataNorm), replace=TRUE, prob=c(0.7, 0.3))
trainData <- dataNorm[ind==1,]
testData <- dataNorm[ind==2,]
# Execution of k-NN with k=1
KnnTestPrediction_k1 <- knn(trainData[,-5], testData[,-5],
trainData$Species, k=1, prob=TRUE)
# Execution of k-NN with k=2
KnnTestPrediction_k2 <- knn(trainData[,-5], testData[,-5],
trainData$Species, k=2, prob=TRUE)
# Execution of k-NN with k=3
KnnTestPrediction_k3 <- knn(trainData[,-5], testData[,-5],
trainData$Species, k=3, prob=TRUE)
# Execution of k-NN with k=4
KnnTestPrediction_k4 <- knn(trainData[,-5], testData[,-5],
trainData$Species, k=4, prob=TRUE)
# Confusion matrix of KnnTestPrediction_k1
table(testData$Species, KnnTestPrediction_k1)
## KnnTestPrediction_k1
## setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 12 0
## virginica 0 2 14
# Classification accuracy of KnnTestPrediction_k1
sum(KnnTestPrediction_k1==testData$Species)/length(testData$Species)*100
## [1] 94.73684
# Confusion matrix of KnnTestPrediction_k2
table(testData$Species, KnnTestPrediction_k2)
## KnnTestPrediction_k2
## setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 12 0
## virginica 0 3 13
# Classification accuracy of KnnTestPrediction_k2
sum(KnnTestPrediction_k2==testData$Species)/length(testData$Species)*100
## [1] 92.10526
# Confusion matrix of KnnTestPrediction_k3
table(testData$Species, KnnTestPrediction_k3)
## KnnTestPrediction_k3
## setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 12 0
## virginica 0 2 14
# Classification accuracy of KnnTestPrediction_k3
sum(KnnTestPrediction_k3==testData$Species)/length(testData$Species)*100
## [1] 94.73684
# Confusion matrix of KnnTestPrediction_k4
table(testData$Species, KnnTestPrediction_k4)
## KnnTestPrediction_k4
## setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 12 0
## virginica 0 2 14
# Classification accuracy of KnnTestPrediction_k4
sum(KnnTestPrediction_k4==testData$Species)/length(testData$Species)*100
## [1] 94.73684
# Empty variables
KnnTestPrediction <- list()
accuracy <- numeric()
# From k=1 to k=100...
for(k in 1:100){
# KnnTestPrediction for each k
KnnTestPrediction[[k]] <- knn(trainData[,-5], testData[,-5], trainData$Species, k, prob=TRUE)
# Accuracy for each k
accuracy[k] <- sum(KnnTestPrediction[[k]]==testData$Species)/length(testData$Species)*100
}
# Accuracy vs Choice of k
plot(accuracy, type="b", col="dodgerblue", cex=1, pch=20,
xlab="k, number of neighbors", ylab="Classification accuracy",
main="Accuracy vs Neighbors")
# Add lines indicating k with best accuracy
abline(v=which(accuracy==max(accuracy)), col="darkorange", lwd=1.5)
# Add line for max accuracy seen
abline(h=max(accuracy), col="grey", lty=2)
# Add line for min accuracy seen
abline(h=min(accuracy), col="grey", lty=2)
