Adjusting the Outputs of a Classifier to New a Priori Probabilities¶
Saerens, Latinne, & Decaestecker. (2002). Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure. Neural Computation.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Label Shift
- Classes become more or less prevalent in the deployment environment than they were in training; decision thresholds tuned at the training prior, calibrations that encode the training class balance, and Bayes-optimal score cutoffs all quietly miscalibrate without any change to the model's weights
This sourceA procedure that adjusts a classifier's outputs, without refitting it, when the a priori class probabilities differ between the training and deployment data.
Supported in partVerified against the publisher's abstract
“It sometimes happens (for instance in case control studies) that a classifier is trained on a data set that does not reflect the true a priori probabilities of the target classes on real-world data”
- Classes become more or less prevalent in the deployment environment than they were in training; decision thresholds tuned at the training prior, calibrations that encode the training class balance, and Bayes-optimal score cutoffs all quietly miscalibrate without any change to the model's weights
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