Decision Tree Visualizer - ML Learning Tool

📚 What is a Decision Tree?

A decision tree is a flowchart-like structure where each internal node represents a test on a feature, each branch represents the outcome of the test, and each leaf node represents a class label. Decision trees learn by recursively splitting data to maximize information gain.

Real-world applications:

Medical diagnosis (symptom-based disease prediction)
Credit approval (risk assessment)
Customer segmentation (marketing)
Fraud detection (banking)
Game AI (decision making)

🎮 Controls

Max Tree Depth:

Min Samples for Split:

Split Criterion:

Add Training Data:

📊 Sample Datasets:

Tree Depth: -

Total Nodes: -

Leaf Nodes: -

Accuracy: -

Data Points: 0

🌳 Decision Tree Visualizer

📚 What is a Decision Tree?

🎮 Controls

Training Data & Decision Regions

Decision Tree Structure