data requirements
calibration set
Requires a calibration dataset for adjustment
6 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Conformal Prediction | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Conformal prediction wraps any trained model so that, instead of a single answer, it returns a set of plausible answers... | ||
| Platt Scaling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Platt scaling turns a classifier's raw scores into calibrated probabilities, meaning that among the cases given a... | ||
| Temperature Scaling | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | Temperature scaling is a correction applied after training to a classifier that is more confident than it is accurate, a... | ||
| Threshold Optimiser | Algorithmic | Architecture/model Agnostic Paradigm/discriminative +2 | Any | Threshold Optimiser is a post-processing method, introduced by Hardt, Price and Srebro (2016), that adjusts a trained... | ||
| Calibration with Equality of Opportunity | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Calibration with equality of opportunity is a post-processing step that adjusts a classifier's scores so that two groups... | ||
| Isotonic Regression Calibration | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Isotonic regression calibration turns a classifier's scores into calibrated probabilities by fitting a step-shaped... |
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