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In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines,^[1] replacing an earlier method by Vapnik, but can be applied to other classification models.^[2] Platt scaling works by fitting a logistic regression model to a classifier's scores.

^ Platt, John (1999). "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods". Advances in Large Margin Classifiers. 10 (3): 61–74.
^ Niculescu-Mizil, Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430.