Artificial intelligence · University
Logistic scores, thresholds and confusion matrices · revision notes
A logistic model maps a real score through σ(z)=1/(1+exp(−z)). Interpreting a score as a reliable probability requires calibration assessment.
Key ideas and conditions
Score
A logistic model maps a real score through σ(z)=1/(1+exp(−z)). Interpreting a score as a reliable probability requires calibration assessment.
Threshold
A binary decision compares a score with a stated threshold. Changing the threshold can trade false positives against false negatives.
Metrics
Precision is TP/(TP+FP); recall is TP/(TP+FN). A zero denominator requires explicit handling rather than silent division.
F1
F1=2TP/(2TP+FP+FN) where defined. It summarizes a tradeoff but does not incorporate every application-specific cost.
Explain without looking
- Should a metric with zero denominator be silently reported as an ordinary exact value?
- Why should a threshold be selected using validation rather than repeated final-test tuning?
Further reading and review references
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