IMPROVEMENTS_V2.md

Advanced Model Optimization - Version 2

Key Improvements Made

1. Removed Timeout Barrier ✅

• Before: 1-hour timeout limit
• After: No timeout - model will complete all iterations
• Impact: Allows full optimization without interruption

2. Increased Optimization Trials ✅

• Before: 100 trials per model
• After: 300 trials per model (3x more)
• Impact: Better hyperparameter search, higher chance of finding optimal parameters

3. Balanced Accuracy + Recall Optimization ✅

• Before: Optimized only for recall (0.5 * accuracy + 0.5 * recall)
• After: Balanced optimization (0.4 * accuracy + 0.6 * recall) with smart penalties
• Features:
  • Penalizes if recall is too low relative to accuracy
  • Bonus if both accuracy > 85% AND recall > 90%
  • Penalty if accuracy drops below 80%
• Impact: Should improve both metrics simultaneously

4. Improved Threshold Optimization ✅

• Before: Simple combined metric
• After: Balanced threshold optimization that:
  • Rewards high recall but penalizes if accuracy drops too much
  • Gives bonus for high performance in both metrics
  • Prevents accuracy from dropping below acceptable levels

Expected Results

With these improvements, we expect:

• Accuracy: 84-86% (improved from 81.9%)
• Recall: 90-93% (maintained high recall)
• F1 Score: 85-87% (improved balance)
• ROC-AUC: 92-93% (maintained or improved)

Training Configuration

• Trials per model: 300 (XGBoost, CatBoost, LightGBM)
• Total trials: 900
• Timeout: None (will complete all trials)
• Memory limit: 4GB
• CPU limit: 2 cores
• Estimated time: 3-6 hours (depending on CPU performance)

Monitoring Progress

Check progress with:

tail -f optimization_v2_log.txt

Or check Docker logs:

docker logs -f heart-optimization-v2

What's Different

1. No timeout - Training will complete all 300 trials per model
2. Better scoring - Optimizes for both accuracy AND recall
3. Smarter threshold - Finds thresholds that balance both metrics
4. More exploration - 3x more trials = better hyperparameter space coverage

Expected Timeline

• XGBoost (300 trials): ~1.5-2 hours
• CatBoost (300 trials): ~2-3 hours
• LightGBM (300 trials): ~1-1.5 hours
• Threshold optimization: ~5 minutes
• Ensemble optimization: ~10 minutes
• Total: ~4.5-6.5 hours

The model will automatically save results when complete!