# ✅ ENHANCED LEADERBOARD API - COMPLETE

## 🎯 User Request Fulfilled

Your requested code pattern now works perfectly:

```python
models = {
    "LogReg": LogisticRegression(max_iter=1000, class_weight="balanced", random_state=SEED),
    "RandomForest": RandomForestClassifier(n_estimators=300, class_weight="balanced", random_state=SEED),
}
if HAS_XGB:
    models["XGBoost"] = XGBClassifier(
        tree_method="hist", enable_categorical=False, random_state=SEED
    )

comparison_results = ml.compare_models(
    models=models,
    X_train=X_train, y_train=y_train,
    X_test=X_test,   y_test=y_test,
    cv_folds=5,
    scoring=SCORING
)

_ = ml.display_leaderboard(
    comparison_results=comparison_results,
    sort_by=PRIMARY, ascending=False, show_std=True, highlight_best=True
)
```

## 🚀 Enhanced Features

### compare_models() Enhancements:
✅ **X_test, y_test parameters**: Now supports test data evaluation  
✅ **cv_folds parameter**: Cross-validation fold configuration  
✅ **scoring parameter**: Custom scoring metric specification  
✅ **Prioritizes test over validation**: Uses test data if provided  
✅ **Auto problem detection**: Automatically detects classification vs regression  

### display_leaderboard() Enhancements:
✅ **comparison_results parameter**: Direct results from compare_models()  
✅ **sort_by parameter**: Sort by any metric column  
✅ **ascending parameter**: Control sort order  
✅ **show_std parameter**: Show/hide standard deviation columns  
✅ **highlight_best parameter**: Highlight top performing model  
✅ **Backward compatibility**: Still supports old ranked_df parameter  

## 🧪 Validation Results
- ✅ **API Pattern**: Your exact code works without modifications
- ✅ **Parameters**: All requested parameters supported
- ✅ **Functionality**: Models compared correctly using test data
- ✅ **Display**: Leaderboard shows results with sorting and highlighting
- ✅ **Integration**: Works seamlessly with existing edaflow ML workflow

## 📈 Next Steps
The enhanced API is now ready for:
1. **Production use** with your exact code pattern
2. **PyPI deployment** as part of v0.14.2 
3. **Documentation updates** with new parameter examples
4. **User adoption** with improved ML workflow experience

**🎉 Your requested leaderboard API enhancements are complete and working!**
