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---
name: aline_d_estimator
description: Solve the ALine-D pairwise linear system to predict OOD accuracies from ID accuracy and unlabeled agreement statistics.
---
# ALine-D Estimator
Use this skill after agreement statistics and an agreement-line slope are available. It implements the paper's ALine-D estimator rather than a simple plug-in baseline.
## Inputs
- Statistics JSON from `agreement_statistics`.
- Fit JSON from `agreement_line_fit`.
## Outputs
- Predicted OOD accuracy per model.
- ALine-S baseline predictions.
- Equation count and residual diagnostics.
## Workflow
1. Build one linear equation for every unordered model pair.
2. Use the fitted agreement slope and pairwise agreement terms from Equation (6).
3. Solve the least-squares system over probit OOD accuracies.
4. Convert predictions back to probability space.
## Validation
Run `python tests/test_aline_d_estimator.py` from this skill directory.
## Limitations
At least three models are required. Rank-deficient synthetic cases may fail because the system cannot identify every model's OOD accuracy.
