Reference · Glossary
Statistics for ML
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Evaluating classifiers, comparing A/B model tests, checking class imbalance, reporting precision/recall, and interpreting benchmark tables.
#When to use
Evaluating classifiers, comparing A/B model tests, checking class imbalance, reporting precision/recall, and interpreting benchmark tables.
#When not to
As a substitute for domain review — a statistically significant metric can still be useless if it ignores business cost.
#Example
1000 emails, 50 spam (5%)
Model always predicts "not spam" → 95% accuracy but catches 0 spam
→ need precision, recall, and confusion matrix — not accuracy alonePair summary stats with **splits**, **confidence**, and **error analysis**.