IMPROVEMENTS.md

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

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

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:

# 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