Kernel SVM

Importing the dataset

dataset = read.csv("C:/RClass/Social_Network_Ads.csv")
dataset = dataset[3:5]

Encoding the target feature as factor

dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1))

Splitting the dataset into the Training set and Test set

# install.packages('caTools')
library(caTools)

## Warning: package 'caTools' was built under R version 4.1.3

set.seed(123)
split = sample.split(dataset$Purchased, SplitRatio = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

Feature Scaling

training_set[-3] = scale(training_set[-3])
test_set[-3] = scale(test_set[-3])

Fitting Kernel SVM to the Training set

# install.packages('e1071')
library(e1071)

## Warning: package 'e1071' was built under R version 4.1.3

classifier = svm(formula = Purchased ~ .,
                 data = training_set,
                 type = 'C-classification',
                 kernel = 'radial')

Predicting a new result

y_pred = predict(classifier, newdata = test_set[-3])

Making the Confusion Matrix

cm = table(test_set[, 3], y_pred)

Visualising the Training set results

#install.packages('ElemStatLearn')
#library(ElemStatLearn)
#set = training_set
#X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
#X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
#grid_set = expand.grid(X1, X2)
#colnames(grid_set) = c('Age', 'EstimatedSalary')
#y_grid = predict(classifier, newdata = grid_set)
#plot(set[, -3],
#     main = 'Kernel SVM (Training set)',
#     xlab = 'Age', ylab = 'Estimated Salary',
#     xlim = range(X1), ylim = range(X2))
#contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)
#points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'dodgerblue', 'salmon'))
#points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'dodgerblue3', 'salmon3'))

Visualising the Test set results

#library(ElemStatLearn)
#set = test_set
#X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
#X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
#grid_set = expand.grid(X1, X2)
#colnames(grid_set) = c('Age', 'EstimatedSalary')
#y_grid = predict(classifier, newdata = grid_set)
#plot(set[, -3], main = 'Kernel SVM (Test set)',
#     xlab = 'Age', ylab = 'Estimated Salary',
#     xlim = range(X1), ylim = range(X2))
#contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)
#points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'dodgerblue', 'salmon'))
#points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'dodgerblue3', 'salmon3'))