Decision Tree Classifiers in R Programming

Classification is the task in which objects of several categories are categorized into their respective classes using the properties of classes. A classification model is typically used to,

• Predict the class label for a new unlabeled data object
• Provide a descriptive model explaining what features characterize objects in each class

There are various types of classification techniques such as,

• Logistic Regression
• Decision Tree
• K-Nearest Neighbours
• Naive Bayes Classifier
• Support Vector Machines (SVM)
• Random Forest Classification

Decision Tree Classifiers in R Programming

A decision tree is a flowchart-like tree structure in which the internal node represents feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. A Decision Tree consists of,

• Nodes: Test for the value of a certain attribute.
• Edges/Branch: Represents a decision rule and connect to the next node.
• Leaf nodes: Terminal nodes that represent class labels or class distribution.

And this algorithm can easily be implemented in the R language. Some important points about decision tree classifiers are,

• It is more interpretable
• Automatically handles decision-making
• Bisects the space into smaller spaces
• Prone to overfitting
• Can be trained on a small training set
• Majorly affected by noise

Implementation in R

The Dataset:

A sample population of 400 people shared their age, gender, and salary with a product company, and if they bought the product or not(0 means no, 1 means yes). Download the dataset Advertisement.csv.

R

# Importing the dataset
dataset = read.csv('Advertisement.csv')
head(dataset, 10)

Output:

	User ID	Gender	Age	EstimatedSalary	Purchased
0	15624510	Male	19	19000	0
1	15810944	Male	35	20000	0
2	15668575	Female	26	43000	0
3	15603246	Female	27	57000	0
4	15804002	Male	19	76000	0
5	15728773	Male	27	58000	0
6	15598044	Female	27	84000	0
7	15694829	Female	32	150000	1
8	15600575	Male	25	33000	0
9	15727311	Female	35	65000	0

Train the data

To train the data we will split the dataset into a test set and then make Decision Tree Classifiers with rpart package.

R

# 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)
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 Decision Tree Classification
# to the Training set
# install.packages('rpart')
library(rpart)
classifier = rpart(formula = Purchased ~ .,
                   data = training_set)

# Predicting the Test set results
y_pred = predict(classifier,
                 newdata = test_set[-3],
                 type = 'class')

# Making the Confusion Matrix
cm = table(test_set[, 3], y_pred)

• The training set contains 300 entries.
• The test set contains 100 entries.

Confusion Matrix:
[[62,  6],
 [ 3, 29]]

Visualizing the Train Data:

R

# Visualising the Training set results
# Install ElemStatLearn if not present
# in the packages using(without hashtag)
# install.packages('ElemStatLearn')
library(ElemStatLearn)
set = training_set

# Building a grid of Age Column(X1)
# and Estimated Salary(X2) Column
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)

# Give name to the columns of matrix
colnames(grid_set) = c('Age',
                       'EstimatedSalary')

# Predicting the values and plotting them
# to grid and labelling the axes
y_grid = predict(classifier,
                 newdata = grid_set,
                 type = 'class')
plot(set[, -3],
     main = 'Decision Tree
             Classification (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'))

Output:

Visualizing the Test Data:

R

# Visualising the Test set results
library(ElemStatLearn)
set = test_set

# Building a grid of Age Column(X1)
# and Estimated Salary(X2) Column
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)

# Give name to the columns of matrix
colnames(grid_set) = c('Age',
                       'EstimatedSalary')

# Predicting the values and plotting them
# to grid and labelling the axes
y_grid = predict(classifier,
                 newdata = grid_set,
                 type = 'class')
plot(set[, -3], main = 'Decision Tree
                        Classification (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,
                    'springgreen3',
                    'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1,
                                  'green4',
                                  'red3'))

Output:

Decision Tree Diagram:

R

# Plotting the tree
plot(classifier)
text(classifier)

Output: