Machine learning · Intermediate · 6 h

Evaluating models honestly

Accuracy traps, ROC AUC, precision and recall, thresholds and lift, on a real imbalanced problem.

Why employers care: Choosing the wrong metric is the most common reason models fail in production. Interviewers probe it directly.

In the roadmaps for: Data Scientist, AI/ML Engineer

Lessons, in order

Evaluating a classifier on imbalanced data89% accuracy that is worse than useless. Precision, recall, ROC AUC, thresholds and lift on 45,211 real bank calls.3 min

Next shelf: Features, leakage and cross-validation