Gradient Boosting Demo

AIO2025: Module 03.

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About this Gradient Boosting Demo

This interactive demo showcases Gradient Boosting algorithms for both classification and regression tasks. Explore sequential ensemble learning where each tree corrects errors of previous trees through dynamic parameter adjustment, comprehensive visualizations, and real-time predictions.

🚀 How to Use: Select data → Configure target → Set boosting parameters → Enter new point → Run prediction!

Start with sample datasets or upload your own CSV/Excel files.

📁 Upload Your Data

🗂️ Sample Datasets

🎯 Target Column

🔄 Loading sample data...

📋 Data Preview (First 5 Rows)

🚀 Gradient Boosting Parameters

Number of Iterations

Number of boosting iterations (limited to 15)

Learning Rate

Step size shrinkage to prevent overfitting

0.01 1

Max Depth

Maximum depth of individual trees (shallow trees work best)

🎯 Criterion

Objective to measure split quality (auto-switched for regression)

Min Samples Split

Minimum samples required to split an internal node

Min Samples Leaf

Minimum samples required to be at a leaf node

🚀 Gradient Boosting Results & Visualization

Boosting Progress: How Prediction Evolves

🚀 Select Iteration to Visualize

Individual Iteration Tree Structure

Feature Importance

**🚀 Boosting Process**

Boosting details will appear here showing how the prediction builds up.

🚀 Gradient Boosting Tips:

📈 Boosting Progress Chart: Shows how predictions evolve sequentially as each tree corrects previous errors.
🚀 Individual Iteration Visualization: Select any iteration to see the tree that was added at that stage.
📊 Feature Importance: Displays which features matter most across all boosting iterations.
🎯 Parameter Tuning: Try different number of iterations (limited to 15) and learning rate (0.01-1.0) to see changes.
⚡ Learning Rate Control: Lower learning rates require more iterations but often lead to better performance.
🌱 Shallow Trees: Max depth of 3-5 typically works best for gradient boosting (weak learners).
🛡️ Overfitting Prevention: Learning rate and min samples parameters help control complexity.
🔍 Sequential Analysis: Use the iteration selector to see how each tree contributes to the final prediction.