XGBoost Demo

AIO2025: Module 03.

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About this XGBoost Demo

This interactive demo showcases XGBoost (Extreme Gradient Boosting) algorithms for both classification and regression tasks. Explore advanced gradient boosting with optimized performance, regularization techniques, and comprehensive visualizations for real-time predictions.

🚀 How to Use: Select data → Configure target → Set XGBoost 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)

🚀 XGBoost Parameters

Number of Trees

Number of boosting trees (up to 100)

Learning Rate (eta)

Step size shrinkage for gradient descent

0.01 1

Max Depth

Maximum depth of individual trees

Min Child Weight

Minimum sum of instance weight needed in a child

Subsample

Subsample ratio of training instances

0.5 1

Colsample by Tree

Subsample ratio of columns when constructing each tree

0.5 1

📊 Data Split Configuration

Train/Validation Split Ratio

Proportion of data used for training (e.g., 0.8 = 80% train, 20% validation)

0.6 0.9

Display train/validation set information

Show Split Details

🚀 XGBoost Results & Visualization

XGBoost Progress: How Prediction Evolves

Training/Validation Loss Evolution

🌳 Select Tree to Visualize

Individual Tree Structure

Feature Importance

**🚀 XGBoost Process**

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

🚀 XGBoost Tips:

📈 Progress Chart: Shows how predictions evolve as each tree is added to the ensemble.
📉 Loss Chart: Monitor training and validation loss to understand model convergence.
🌳 Individual Tree Visualization: Select any tree to see its structure and contribution.
📊 Feature Importance: Displays which features are most influential across all trees.
🎯 Parameter Tuning: Try different number of trees (up to 100) and learning rate (0.01-1.0).
⚡ Learning Rate (eta): Default 0.3 works well; lower values need more trees but better generalization.
🌲 Tree Depth: Max depth 6 is default; deeper trees can capture more complex patterns but may overfit.
🛡️ Regularization: Min child weight, subsample, and colsample_bytree help prevent overfitting.
🔍 Tree Analysis: Use the tree selector to understand how each tree contributes to predictions.