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.

 Output:

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.

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

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

Visualizing the Train Data:

 Output:

Visualizing the Test Data:

 Output:

Decision Tree Diagram:

 Output: