# Model Improvement Analysis & Recommendations

## Current Performance Summary

Based on the existing models:

| Model | Accuracy | Precision | Recall | F1 | ROC-AUC |
|-------|----------|-----------|--------|-----|---------|
| XGBoost_best | 0.849 | 0.853 | 0.843 | 0.848 | 0.925 |
| CatBoost_best | 0.851 | 0.857 | 0.842 | 0.849 | 0.925 |
| LightGBM_best | 0.851 | 0.857 | 0.843 | 0.850 | 0.925 |
| Ensemble_best | 0.850 | 0.855 | 0.843 | 0.849 | 0.925 |

## Identified Improvement Opportunities

### 1. **Hyperparameter Optimization** ⭐⭐⭐
**Current State:**
- Using `RandomizedSearchCV` with limited iterations (20-25)
- Limited parameter search spaces
- Scoring only on `roc_auc`

**Improvements:**
- ✅ **Optuna-based optimization** (implemented in `improve_models.py`)
  - Tree-structured Parzen Estimator (TPE) sampler
  - Median pruner for early stopping
  - 100+ trials per model
  - Expanded hyperparameter ranges

**Expected Impact:** +1-3% accuracy, +1-2% recall

### 2. **Multi-Objective Optimization** ⭐⭐⭐
**Current State:**
- Optimizing only for ROC-AUC
- No explicit focus on recall (critical for medical diagnosis)

**Improvements:**
- ✅ **Combined scoring function** (0.5 * accuracy + 0.5 * recall)
- ✅ **Threshold optimization** for each model
- ✅ **Recall-focused tuning**

**Expected Impact:** +2-4% recall improvement

### 3. **Threshold Optimization** ⭐⭐
**Current State:**
- Using default threshold of 0.5 for all models
- No model-specific threshold tuning

**Improvements:**
- ✅ **Per-model threshold optimization**
- ✅ **Ensemble threshold optimization**
- ✅ **Metric-specific threshold tuning** (F1, recall, combined)

**Expected Impact:** +1-3% recall, +0.5-1% accuracy

### 4. **Expanded Hyperparameter Search Spaces** ⭐⭐
**Current State:**
- Limited parameter ranges
- Missing important hyperparameters

**Improvements:**
- ✅ **XGBoost:** Added `colsample_bylevel`, `gamma`, expanded ranges
- ✅ **CatBoost:** Added `border_count`, `bagging_temperature`, `random_strength`
- ✅ **LightGBM:** Added `min_split_gain`, expanded `num_leaves` range

**Expected Impact:** +0.5-2% overall improvement

### 5. **Feature Engineering & Selection** ⭐⭐
**Current State:**
- Using all features without analysis
- No feature importance-based selection

**Improvements:**
- ✅ **Feature importance analysis** (implemented in `feature_importance_analysis.py`)
- ✅ **Statistical feature selection** (F-test, Mutual Information)
- ✅ **Combined importance scoring**
- 🔄 **Feature selection experiments** (can be added)

**Expected Impact:** +0.5-1.5% accuracy, potential overfitting reduction

### 6. **Ensemble Optimization** ⭐⭐
**Current State:**
- Simple 50/50 weighting for XGBoost and CatBoost
- No optimization of ensemble weights

**Improvements:**
- ✅ **Grid search for optimal weights**
- ✅ **Three-model ensemble** (XGBoost + CatBoost + LightGBM)
- ✅ **Weight optimization with threshold tuning**

**Expected Impact:** +0.5-1.5% accuracy, +0.5-1% recall

### 7. **Early Stopping & Regularization** ⭐
**Current State:**
- Fixed number of estimators
- Basic regularization

**Improvements:**
- ✅ **Optuna pruner** (MedianPruner)
- ✅ **Enhanced regularization** (expanded ranges)
- 🔄 **Early stopping callbacks** (can be added)

**Expected Impact:** Better generalization, reduced overfitting

## Implementation Guide

### Step 1: Run Advanced Optimization
```bash
python improve_models.py
```

This will:
- Run Optuna optimization for all three models (100 trials each)
- Optimize thresholds for each model
- Optimize ensemble weights
- Save optimized models and results

**Time:** ~1-2 hours (depending on hardware)

### Step 2: Analyze Feature Importance
```bash
python feature_importance_analysis.py
```

This will:
- Extract feature importance from all models
- Perform statistical feature selection
- Generate recommendations
- Create visualizations

**Time:** ~5-10 minutes

### Step 3: Compare Results
Compare the new `model_metrics_optimized.csv` with existing `model_metrics_best.csv`:
```bash
# View optimized results
cat content/models/model_metrics_optimized.csv

# Compare with previous best
cat content/models/model_metrics_best.csv
```

## Additional Recommendations

### 1. **Advanced Feature Engineering**
- Polynomial features for key interactions (age × BP, BMI × cholesterol)
- Binning continuous features
- Domain-specific features (e.g., Framingham Risk Score components)

### 2. **Advanced Ensemble Methods**
- **Stacking:** Use meta-learner to combine base models
- **Blending:** Weighted average with learned weights
- **Voting:** Hard/soft voting ensembles

### 3. **Data Augmentation**
- SMOTE for minority class oversampling
- ADASYN for adaptive synthetic sampling
- BorderlineSMOTE for better boundary examples

### 4. **Cross-Validation Strategy**
- Nested cross-validation for unbiased evaluation
- Time-based splits (if temporal data)
- Group-based splits (if group structure exists)

### 5. **Model Calibration**
- Platt scaling
- Isotonic regression
- Temperature scaling

### 6. **Hyperparameter Tuning Enhancements**
- Multi-objective optimization (Pareto front)
- Bayesian optimization with Gaussian processes
- Hyperband for faster search

## Expected Overall Improvement

With all improvements implemented:

| Metric | Current | Expected | Improvement |
|--------|---------|----------|-------------|
| Accuracy | 0.851 | 0.860-0.870 | +1-2% |
| Recall | 0.843 | 0.860-0.875 | +2-4% |
| F1 Score | 0.850 | 0.860-0.870 | +1-2% |
| ROC-AUC | 0.925 | 0.930-0.935 | +0.5-1% |

## Files Created

1. **`improve_models.py`** - Main optimization script
2. **`feature_importance_analysis.py`** - Feature analysis script
3. **`IMPROVEMENTS.md`** - This document

## Next Steps

1. ✅ Run `improve_models.py` to get optimized models
2. ✅ Run `feature_importance_analysis.py` for feature insights
3. 🔄 Test optimized models on validation set
4. 🔄 Compare with baseline models
5. 🔄 Deploy best performing model
6. 🔄 Monitor performance in production

## Notes

- The optimization scripts are designed to be run independently
- Results are saved to `content/models/` directory
- All improvements are backward compatible
- Existing models are not overwritten (new files with `_optimized` suffix)

## Troubleshooting

**Issue:** Optuna optimization takes too long
- **Solution:** Reduce `n_trials` in `improve_models.py` (e.g., 50 instead of 100)

**Issue:** Memory errors during optimization
- **Solution:** Reduce `n_jobs` or use smaller data sample

**Issue:** No improvement in metrics
- **Solution:** Check if data preprocessing matches training data
- Verify feature alignment
- Check for data leakage