Stop Trusting Accuracy — F1 Score & AUROC Explained Simply

Your model says it's 99% accurate — but is it actually good? In this video, we break down two of the most important (and most misunderstood) metrics in machine learning: F1 Score and AUROC. You'll learn what Precision and Recall really mean, why accuracy can be misleading on imbalanced datasets, and why F1 uses the harmonic mean instead of a simple average to catch models that are secretly failing. Then we cover the ROC curve, True Positive Rate vs False Positive Rate, and the intuitive way to think about AUROC — the probability your model ranks a random positive example higher than a random negative one. We finish with when to use F1 vs AUROC, and where each one can mislead you.