Datasets:
| { | |
| "field_id": "F046", | |
| "field": "conformal prediction", | |
| "directions": [ | |
| { | |
| "direction_id": "G0", | |
| "name": "Coverage-calibrated predictive sets and intervals", | |
| "scope": "Construct and evaluate prediction sets or intervals with coverage guarantees for model predictions, including general-purpose conformal calibration, local calibration for covariate or response heterogeneity, and the use of predictive distributions, representations, classifier information, or region geometry to improve the usefulness of the sets." | |
| }, | |
| { | |
| "direction_id": "G1", | |
| "name": "Predictive guarantees for dependent and evolving data", | |
| "scope": "Establish or maintain conformal prediction guarantees under temporal dependence, sequential arrival, or changing observation conditions, including dependence-aware validity, sequential recalibration, and joint prediction regions across a forecasting horizon." | |
| }, | |
| { | |
| "direction_id": "G2", | |
| "name": "Conformal loss and decision-risk control", | |
| "scope": "Calibrate predictive outputs, sets, or decisions to guarantee bounds on expected loss, error risk, or decision cost, rather than only guaranteeing that the true outcome falls within a prediction set." | |
| } | |
| ] | |
| } | |