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Decision Tree Classification.R
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Decision Tree Classification.R
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# ---------------------------------------------------- Importing Data ------------------------------------------ #
dataset = read.csv('Social_Network_Ad.csv')
# Selecting particular columns
dataset = dataset[3:5]
# ---------------------------------------- Encoding the target feature as factor ------------------------------- #
dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1))
# ---------------------------------- Splitting the dataset into Training 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 Scalling ----------------------------------------- #
training_set[-3] = scale(training_set[-3])
test_set[-3] = scale(test_set[-3])
# ------------------------------------- Fitting Classifier to the Training Set --------------------------------- #
library(rpart)
classifier = rpart(Purchased ~ ., data = training_set)
summary(classifier)
# -------------------------------------------- Predicting the Test Set result ---------------------------------- #
y_pred = predict(classifier, newdata = test_set[-3], type = 'class')
y_pred
# ------------------------------------------------- Confusion Matrix ------------------------------------------- #
cm = table(test_set[, 3], y_pred)
cm
# --------------------------------------- Visualising the Training Set results --------------------------------- #
library(ElemStatLearn)
set = training_set
train1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
train2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
grid_set = expand.grid(train1, train2)
colnames(grid_set) = c('Age', 'EstimatedSalary')
y_grid = predict(classifier, newdata = grid_set, type = 'class')
# Plotting
plot(set[, -3],
main = 'Decision Tree Classification (Training set)',
xlab = 'Age', ylab = 'Estimated Salary',
xlim = range(train1), ylim = range(train2))
# Regression Line
contour(train1, train2, matrix(as.numeric(y_grid), length(train1), length(train2)), add = TRUE)
# Giving Colour
points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))
# ------------------------------------------- Visualising the Test Set results --------------------------------- #
library(ElemStatLearn)
set = test_set
test1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
test2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
grid_set = expand.grid(test1, test2)
colnames(grid_set) = c('Age', 'EstimatedSalary')
y_grid = predict(classifier, newdata = grid_set, type = 'class')
# Plotting
plot(set[, -3], main = 'Decision Tree Classification (Test set)',
xlab = 'Age', ylab = 'Estimated Salary',
xlim = range(test1), ylim = range(test2))
# Regression Line
contour(test1, test2, matrix(as.numeric(y_grid), length(test1), length(test2)), add = TRUE)
# Giving Colour
points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))
# ---------------------------------------------- Plotting Classifier ------------------------------------------ #
# Run this below code removing all the thing from the environment and without running feature scalling.
plot(classifier)
text(classifier)