Model Evaluation Metrics: Precision, Recall, F1-Score, AUC-ROC Explained

TLDR: 🎯 Accuracy is a lie when classes are imbalanced. Real ML evaluation uses precision (how many positives are actually positive), recall (how many actual positives we caught), F1 (their balance), and AUC-ROC (performance across all thresholds). T...