- For classifiers, recalibration is also known as probability calibration and is typically used to correct overconfident or underconfident classifiers.
- RecalibrationFunction can be used at training time, inference time or to update the calibrator of an existing model.
- When specified at inference time or to update the calibrator of an existing model, typical settings for RecalibrationFunction include:
None remove existing recalibration f append the function f to the existing calibrator
- For classifiers, the function f is applied to the class probabilities f[<|class1p1,class2p2,…|>] and should return new class probabilities. New class probabilities are automatically normalized.
- For predictors, the function f transforms the predictive distribution by being applied to the output values.
- When specified at training time, typical settings for RecalibrationFunction include:
None prevent any recalibration Automatic recalibrate the model when needed All always recalibrate the model
- Besides being used on the final model, recalibration is also applied to candidate models generated by Classify and Predict during their training procedure.
- When RecalibrationFunctionAll, recalibration is applied to every candidate model.
- When RecalibrationFunctionAutomatic, recalibration is only used for candidate models that need it.
Examplesopen allclose all
Basic Examples (4)
Wolfram Research (2021), RecalibrationFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/RecalibrationFunction.html.
Wolfram Language. 2021. "RecalibrationFunction." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/RecalibrationFunction.html.
Wolfram Language. (2021). RecalibrationFunction. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/RecalibrationFunction.html