Platt Scaling#
Fitting a logistic function to scores to produce calibrated probabilities.
Important
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What it is#
Platt scaling calibrates a classifier by fitting a logistic (sigmoid) function on its raw scores, mapping them to probabilities:
The two parameters a and b are fit by negative log-likelihood on a validation set. It was invented by John Platt for SVMs.
How it relates#
It is a parametric method that assumes a sigmoid-shaped miscalibration. Temperature scaling is essentially its one-parameter, multi-class special case (fixing the slope and dropping the offset), so the two are close cousins.
When to use it#
Platt scaling is a solid default for binary classifiers with monotonic score miscalibration and limited calibration data, since two parameters rarely overfit — but if the true miscalibration isn’t sigmoid-shaped, a more flexible method like isotonic regression fits better.
Theme: Probability Calibration · All terminology
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Mind map — connected ideas
Temperature Scaling · Isotonic Regression · Sigmoid Function · Logistic Regression · Support Vector Machines (SVMs) · Confidence Level
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More in Probability Calibration
Adaptive ECE (Expected Calibration Error with Adaptive Binning) · Brier Score · Calibration quality (Model Calibration) · Expected Calibration Error (ECE) · Isotonic Regression · Maximum Calibration Error (MCE) · Murphy’s Decomposition · Overconfident · Reliability Curves (also called Calibration Curves) · Temperature Scaling · Underconfident
See also
Source article Adapted (context, re-expressed) in our own words from: Platt Scaling (insightful-data-lab.com).