Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods¶
Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers.
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Primes¶
- Calibration
- Making traceability explicit—documenting how a metric traces back to strategic intent—is a calibration practice borrowed from metrology, mirroring the post-hoc adjustment logic Platt (1999) introduced for mapping classifier outputs back to a probabilistic reference.
This sourceIntroduces "Platt scaling": fitting a sigmoid to map raw SVM outputs to calibrated posterior probabilities against a held-out reference — the canonical post-hoc mapping of scores back to a probabilistic standard.
- Making traceability explicit—documenting how a metric traces back to strategic intent—is a calibration practice borrowed from metrology, mirroring the post-hoc adjustment logic Platt (1999) introduced for mapping classifier outputs back to a probabilistic reference.
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