Random Forest (RF)

About:

1. Production-Ready: Professional ML workflow with proper validation

2. User-Friendly: Intuitive step-by-step interface

3. Comprehensive: From data upload to model deployment

4. Educational: Perfect for learning Random Forest concepts

5. Scalable: Handles both single and multi-output problems

Technical Sspecification:

1. Model Configuration: 1) Feature selection (multi-select), b) Target selection (single/multi-output), 3) Hyperparameter tuning: (Number of estimators: 10-200, Max depth: 1-20 or unlimited, Train/test ratio: 50%-90%, Data normalization option).

2. Training & Validation:  1) 3-way data split (Train 60% / Validation 20% / Test 20%),  2) 5-fold cross-validation with statistics, 3) Stratified sampling for balanced splits, 3) Real-time training progress.

3. Performance Analysis: 1) Training, Validation, and Test accuracy, 2) Cross-validation scores with confidence intervals, 3) Confusion matrices with heatmaps, 4) Classification reports (Precision/Recall/F1), 5) Feature importance visualization.

4. Unseen Data Testing: 1) Separate CSV upload for new predictions, 2) Feature compatibility validation, 3) Batch prediction processing, 4) Performance evaluation (if targets available), 5) CSV download of results