Reference · Glossary
Probability for ML
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Classification with uncertain outcomes, calibrating spam filters, choosing decision thresholds, and understanding softmax outputs from LLM token predictions.
#When to use
Classification with uncertain outcomes, calibrating spam filters, choosing decision thresholds, and understanding softmax outputs from LLM token predictions.
#When not to
Deterministic lookups where probability adds no value — e.g. fetching a user record by exact ID.
#Example
Model output: P(spam | email) = 0.91
Threshold 0.5 → label "spam"
Threshold 0.95 → label "not spam" (fewer false positives)Probability connects **predictions**, **thresholds**, and **loss functions** in one framework.