Random Forest Demo

AIO2025: Module 03.

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About this Random Forest Demo

This interactive demo showcases Random Forest algorithms for both classification and regression tasks. Explore ensemble learning with decision trees through dynamic parameter adjustment, comprehensive visualizations, and real-time predictions.

🌲 How to Use: Select data → Configure target → Set forest 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)

🌲 Random Forest Parameters

Number of Trees

Number of trees in the forest (limited to 20)

Max Depth

Set to 0 for unlimited depth

Min Samples Split

Minimum samples required to split an internal node

Min Samples Leaf

Minimum samples required to be at a leaf node

🎯 Criterion

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

Max Features

Number of features to consider for best split

🌲 Random Forest Results & Visualization

Individual Tree Predictions & Confidence Scores

🌳 Select Tree to Visualize

Individual Tree Structure

Feature Importance

**🗳️ Voting Results**

Voting details will appear here for classification tasks.

🌲 Random Forest Tips:

📊 Tree Confidence Chart: Shows confidence scores and predictions for each individual tree in the forest.
🌳 Individual Tree Visualization: Select any tree from the dropdown to see its detailed structure and decision paths.
📈 Feature Importance: Displays which features matter most across all trees in the forest.
🎯 Parameter Tuning: Try different number of trees (limited to 20) and max depth (5-15) to see changes.
🌿 Diversity Control: Max features controls tree diversity - 'sqrt' is often optimal for balanced performance.
🛡️ Overfitting Prevention: Min samples split/leaf parameters help control complexity and reduce overfitting.
🔍 Interactive Analysis: Use the tree selector to explore different trees and understand their decision patterns.