Explain with a case study how to visualize a KNN classification model in R.

512 Asked by SnehaPandey in Data Science , Asked on Dec 20, 2019

Answered by Sneha Pandey

First we import the data and convert the target feature as factor

# Importing the dataset

dataset = read.csv('Social_Network_Ads.csv')

dataset = dataset[3:5]

Now we split, scale and fit the data into a KNN model

# Splitting the dataset into the Training set and Test set

# install.packages('caTools')

library(caTools)

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 K-NN to the Training set and Predicting the Test set results

library(class)

y_pred = knn(train = training_set[, -3],

test = test_set[, -3],

cl = training_set[, 3],

k = 5,

prob = TRUE)

Now we evaluate the model in terms of confusion matrix

# Making the Confusion Matrix

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

Now we visualize the training set results

# Visualising the Training set results

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 = knn(train = training_set[, -3], test = grid_set, cl = training_set[, 3], k = 5)

plot(set[, -3],

main = 'K-NN (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, 'springgreen3', 'tomato'))

points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))

Now we will visualize the test set results.

# Visualising the Test set results