All 7 Model Evaluation Metrics questions
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What are common mistakes to avoid with Model Evaluation Metrics

Intermediate

Answer

A common mistake is relying only on accuracy for imbalanced datasets, where one class is much more common than another, since a model can score high accuracy while ignoring the rare class completely.
Real-world example A fraud detection system uses recall as a key metric because missing a fraudulent transaction is far more costly than a few false alarms, even if overall accuracy looks slightly lower.

Common follow-ups: What is precision and recall, What is a confusion matrix, What is F1 score

Machine Learning Fundamentals Deep Learning Supervised & Unsupervised Learning
All 7 Model Evaluation Metrics questions