Model Evaluation Metrics: Precision, Recall, F1-Score, AUC-ROC Explained
6d ago · 17 min read · 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...
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