Model evaluation metrics are quantitative measures that help us understand how well our neural network performs on unseen data.
Beyond Loss Functions:
• Loss guides training
• Metrics evaluate performance
• Different metrics reveal different strengths/weaknesses
• Choose metrics that match your business goals
⚠️ Common Pitfall
A model with low loss doesn't always mean good performance! Always evaluate with appropriate metrics on test data.
Key Evaluation Metrics
🎯
Accuracy
Percentage of correct predictions overall
🔍
Precision
Of all positive predictions, how many were correct?
📊
Recall (Sensitivity)
Of all actual positives, how many did we find?
⚖️
F1 Score
Harmonic mean of precision and recall
📈
AUC-ROC
Area under the ROC curve - threshold independent
Metrics Comparison - Click to Explore
Balanced Dataset
95%
Accuracy
Imbalanced Dataset
99%
Misleading Accuracy!
Medical Diagnosis
98%
High Recall Needed
Spam Detection
97%
High Precision Needed
Remember: The best metric depends on your specific problem and the cost of different types of errors!