Question

Create a Decision Tree classifier for a sample dataset. Display the results visually using appropriate R plotting functions. Explain the outputs obtained.

14 Feb 2026
Answer :
Word Count : 551
A Decision Tree classifier is a popular machine learning algorithm used for both classification and regression tasks. It works by splitting the dataset into branches based on feature values, creating a tree-like model. In R, the `rpart` library is commonly used for building Decision Tree models, and visualization can be achieved with the `rpart.plot` package. Here's an example of how to create a Decision Tree classifier, visualize the results, and interpret the outputs. ### Step 1: Install necessary libraries First, ensure that the necessary libraries are installed and loaded. You can do this by running: ```R install.packages("rpart") install.packages("rpart.plot") library(rpart) library(rpart.plot) ``` ### Step 2: Load and prepare the dataset Let’s use the `iris` dataset, a well-known dataset in machine learning that contains information about the petal and sepal lengths and widths of different iris species. ```R data(iris) ``` ### Step 3: Build the Decision Tree classifier We can use the `rpart` function to create a Decision Tree model. The target variable is the species of the iris flower, and the features are the sepal and petal
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