What is precision? How many selected results are truly positive
Explain precision using email classification and distinguish accuracy, recall, thresholds, false positives and zero denominators.
Understand the concept
Precision is TP/(TP+FP): true positives among predicted positives. It differs from overall accuracy and recall. Define the positive class and references. Fewer false positives can improve precision, but missed targets still matter; one high ratio is not complete system quality.
The denominator includes true and false selections
Google and scikit-learn distinguish precision and recall. For spam-positive classification, blocked ordinary emails are false positives in the denominator. No positive predictions can produce zero division; disclose the implementation rather than silently reporting perfection. Multiclass reporting needs per-class or averaging details. Everyday precision is not this mathematical definition.
Use a small example to verify the calculation
If six messages are flagged and four are spam, precision is four divided by six; this says nothing about missed spam. This is arithmetic, not a benchmark. Preserve predictions and references and inspect false positives. Match thresholds and sample policies instead of comparing highly selective output with all-output systems. Keep counts when no positive predictions occur.
Observe recall when changing thresholds
Higher thresholds can reduce false alarms and increase misses, so weigh actual consequences. Version thresholds and data and inspect new classes. Include review costs. Retrieval precision additionally needs relevance labels and result scope. Report samples, counts and boundaries so the metric conveys evidence rather than authority through abbreviations.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.