Building a Classification Decision Tree in RStudio

In this blog post, I'll guide you through building a classification decision tree in RStudio. Decision trees are powerful tools for classification problems as they provide a clear and interpretable model for decision-making. We'll use the rpart package in R, which stands for Recursive Partitioning and Regression Trees.

By the end of this guide, you'll have learned how to build a classification decision tree, visualize it, and interpret the results.

Setting up RStudio and Installing Packages

Before we dive into the code, let's ensure that we have the necessary packages installed. We'll need rpart for building the decision tree and rpart.plot to visualize the tree.

To install these packages, run the following commands in your RStudio console:

Using Syntax: install.packages("rpart")

install.packages("rpart.plot")

Once you have installed, load the packages into your R session

Using Syntax: library(rpart)

library(rpart.plot)

Loading the Dataset

In this project we will be using the Iris dataset, this contains data on three species of iris flowers (Setosa, Versicolor, and Virginica) and four features (Sepal Length, Sepal Width, Petal Length, Petal Width). The goal is to classify the species of iris based on these features.

The Species column will be our target variable, and the other columns will serve as features for our decision tree model.

Splitting the Data into Training and Test Sets

We'll split the dataset into training (70%) and test (30%) sets. This will allow us to evaluate the model's performance on unseen data.

In R, set.seed(123) ensures reproducibility in randomized processes. When you build a classification decision tree or perform any process that involves random sampling(such as splitting data into training and testing sets), the set.seed(123) command sets the random number generator to a specific starting point. This means that if you or someone else runs the same code with the same seed (123 in this example), you'll get the same results every time.

Building the Classification Decision Tree

Now, let's build the classification decision tree using the rpart() function. The target variable is Species, and we'll use the other columns as predictors.

Visualizing the Decision Tree

Visualizing the decision tree will help us interpret the model's structure and decision rules. The rpart.plot() function makes it easy to plot the tree.

• The type = 3 arument shows the split labels on the nodes.

• extra =104 displays the percentage of observations that follow each branch.

• fallen.leaves = TRUE ensures that the leaves of the tree are aligned.

Evaluating the Model

To evaluate the model, we'll use the test set. We can make predictions using the predict() function and create a confusion matrix to check how well our model performed.

So, we walked through the process of building a classification decision tree using RStudio. You've learned how to:

• Load and prepare data.

• Build a decision tree using the rpart package.

• Visualize and interpret the decision tree.

• Evaluate the model's performance with confusion matrix and accuracy score.

Decision trees are highly interpretable, but they can also overfit, so be sure to try tuning parameters. You can experiment with hyperparameters such as cp (complexity parameter) to control the size of the tree and avoid overfitting. This foundational method can be applied to various classification problems, and it's a great way to start your journey into machine learning with R.

Feel free to reach out with questions or share your experiences below!