Reliability
Calibration
Techniques that check or correct whether a model's stated confidence matches how often it is right.
5 techniques
5 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| 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... | ||
| Reliability Diagram with Expected Calibration Error | Metric | Architecture/model Agnostic Requirements/black Box +1 | Any | A reliability diagram with expected calibration error (ECE) measures whether a classifier's stated confidence matches... |
Rows per page
Page 1 of 1