Classification Evaluation Metrics — Roadmap Step & Resources
Accuracy, precision, recall, F1 and ROC-AUC, and when each one matters most
- Level: intermediate
- Estimated time: 2 days
- Roadmap: Machine Learning
Before this step
Mini projects
- Train a classifier on an imbalanced dataset and evaluate it with precision, recall and ROC-AUC instead of accuracy alone
Interview questions
- Why is accuracy a misleading metric for a highly imbalanced classification problem?
Study resources
- Confusion Matrix, Clearly Explained!!! (Video) — StatQuest's widely-loved explanation of precision, recall and the confusion matrix.
- Classification Metrics (Article) — The official scikit-learn documentation's reference for classification metrics.
Part of
- Supervised Learning: Classification — section
- Machine Learning roadmap — the full learning path