📍 Why Metrics Matter
Learn how to properly measure model performance—because accuracy alone doesn't tell the full story
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Train-Test-Val Split Game
Why Evaluation Metrics Matter
Training a model is only half the battle. To know if it's actually good, you need the right metrics. Accuracy alone can be misleading - a 95% accurate cancer detector that misses every case is useless!
⚠️ The Accuracy Trap
Real-World Example: Disease Detection
Total patients:1,000
Actually sick:10 (1%)
Healthy:990 (99%)
Lazy Model Strategy:
Predict "healthy" for everyone → 99% accuracy! 🎉
Problem: Missed all 10 sick patients who need treatment!
This is why we need more sophisticated metrics beyond simple accuracy.
📊 Types of Metrics
🎯Classification
Accuracy, Precision, Recall, F1-Score
For: Binary/multi-class prediction
📈Regression
MSE, MAE, RMSE, R²
For: Continuous value prediction
🏆Ranking
AUC-ROC, MAP, NDCG
For: Ordering items by relevance
✨Generation
BLEU, ROUGE, Perplexity
For: Text/image generation quality
🎯 Choosing the Right Metric
Is your data balanced?
Yes: Use Accuracy | No: Use F1-Score or Precision/Recall
Are false positives expensive?
Yes: Optimize for Precision | No: Focus on other metrics
Are false negatives dangerous?
Yes: Optimize for Recall | No: Balance with Precision
Need balance between both?
Yes: Use F1-Score | No: Use task-specific metric