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:
```bash
tail -f optimization_v2_log.txt
```

Or check Docker logs:
```bash
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!