Evaluation Metrics: Accuracy, Precision, Recall & F1

« Supervised vs. Unsupervised Learning Models« Previous Data Ethics, Algorithmic Bias & Privacy »Next »

Understand why accuracy is misleading for imbalanced datasets, and master Precision, Recall, and the F1-Score.

The Accuracy Paradox

If 99% of transactions are legitimate and 1% are fraudulent, a naive model that predicts "legitimate" for every single case has 99% accuracy while catching 0% of fraud! This is why Precision and Recall are mandatory.

Precision: True Positives / (True Positives + False Positives). "When the model predicts fraud, is it right?"

Recall: True Positives / (True Positives + False Negatives). "Did we catch all actual fraud cases?"

Google AdSense Slot (inContentBanner)

Set your Publisher ID in src/config/ads.ts to display live ads.

Test Yourself With Exercise

+15 XP

Which metric measures the proportion of actual positive cases that were correctly identified by the model?

« Previous: Supervised vs. Unsupervised Learning Models Next: Data Ethics, Algorithmic Bias & Privacy »