🌲 Decision Tree Classifier

Interactive visualization of decision tree classification on 2D data

Number of Samples: 200

Max Tree Depth: 5

Min Samples Split: 10

Noise Level: 0.2

Training Data

Decision Boundaries

Decision Tree Structure

Train the model to see the tree structure

📚 How to Use

Generate Data: Create random 2D points with two classes (blue and orange)
Adjust Parameters: Change max depth, min samples, and noise level
Train Tree: Build a decision tree classifier and visualize decision boundaries
Observe: See how the tree splits the feature space into regions
Experiment: Try different depths to see overfitting vs underfitting