📈 Model Performance
Evaluating model performance is crucial to determine how well a machine learning model generalizes to new data. In medical applications (e.g., lesion classification), this ensures clinical usefulness and patient safety.
🧪 Key Performance Metrics
| Metric | Description | Interpretation |
|---|---|---|
| Accuracy | Ratio of correct predictions to total cases | Good for balanced datasets |
| Precision | TP / (TP + FP) | How many predicted positives are true |
| Recall (Sensitivity) | TP / (TP + FN) | Ability to detect true positives |
| Specificity | TN / (TN + FP) | Ability to detect true negatives |
| F1-Score | Harmonic mean of precision and recall | Balances precision & recall |
| AUC (Area Under ROC Curve) | Measures ability to distinguish between classes | Closer to 1 = better |
| Balanced Accuracy | Mean of sensitivity and specificity | Useful for imbalanced datasets |
| Confusion Matrix | Table showing TP, FP, TN, FN | Full picture of model errors |
🧠 In Multi-Class Problems
* Metrics are computed per class, then averaged:
- Macro average: Equal weight to each class
- Micro average: Accounts for class imbalance
- Weighted average: Weighted by class frequency
🛠 Python Snippet (Example with scikit-learn)
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score # Assume y_test = true labels, y_pred = predicted labels print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) # For multi-class AUC (with probabilities) roc_auc_score(y_test, y_proba, multi_class="ovr")
📊 Clinical Relevance
* High sensitivity: Essential in detecting critical conditions (e.g., tumors) * High specificity: Important to avoid false positives * Balanced accuracy: Prevents overestimation in imbalanced data (e.g., rare tumors)
⚠️ Best Practices
* Always report multiple metrics, not just accuracy * Use cross-validation to avoid overfitting * Consider confidence intervals for key metrics