📈 Model Performance

📈 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