# Pareto Interpretation Eval

> This evaluation probes a model's ability to synthesize interpretable surrogate models (decision diagrams) that explicitly trade off prediction fidelity against structural simplicity. It measures how well a method can navigate the Pareto front between accuracy and explainability without collapsing them into a single weighted objective. Use when the user wants to benchmark on Airplane Perception Module (AP), Bank Loan Predictor (BL), Theorem Prover Solvability Predictor (TP), or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/pareto-interpretation-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/pareto-interpretation-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/pareto-interpretation-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/pareto-interpretation-eval

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# pareto-interpretation-eval

> Synthesizing Pareto-Optimal Interpretations for Black-Box Models — Torfah et al. (2021) (arXiv:2108.07307, 2021)

## What this evaluates

This evaluation probes a model's ability to synthesize interpretable surrogate models (decision diagrams) that explicitly trade off prediction fidelity against structural simplicity. It measures how well a method can navigate the Pareto front between accuracy and explainability without collapsing them into a single weighted objective.

## Datasets

- **Airplane Perception Module (AP)** — total ?; splits: test (-1)
- **Bank Loan Predictor (BL)** — total ?; splits: test (-1)
- **Theorem Prover Solvability Predictor (TP)** — total ?; splits: test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Standard classification accuracy: fraction of correctly predicted labels out of total samples in the evaluation set.
- `explainability_score` — range: other
  - A structural measure favoring decision diagrams with fewer nodes (size) and predicates with fewer branchings. Lower raw values indicate higher explainability.

## Input / output format

**Input**: Feature vectors specific to each benchmark: (1) time of day, cloud types, and initial positioning for AP; (2) age and income for BL; (3) percentage of unit clauses and average clause length for TP.

**Output**: A decision diagram (interpretation) representing a synthesized trade-off solution between correctness and explainability.

## Scoring recipe

```python
def evaluate(predictions, gold):
    accuracy = sum(p == g for p, g in zip(predictions, gold)) / len(gold)
    dd = predictions.diagram
    dd_size = count_nodes(dd)
    branching = count_branchings(dd)
    explainability = -(dd_size + branching)  # Higher is better
    return {'accuracy': accuracy, 'explainability': explainability}
```

## Common pitfalls

- Comparing with single-objective methods (e.g., MinDS) is unfair because they do not guarantee exploration of the full Pareto-optimal space.
- Sample sizes are not fixed; they are derived from statistical confidence (δ) and error margin (ε), so direct comparison across different settings requires normalization.
- Explainability is defined structurally (decision diagram size and branching) rather than via standard human-study metrics, limiting direct cross-domain benchmarking.

## Evidence (verbatim from paper)

> We used accuracy for correctness, and explainability measure that favored decision diagrams of smaller size and predicates with a fewer number of branchings.

## Citation

```bibtex
@misc{torfah2021pareto,
  title={Synthesizing Pareto-Optimal Interpretations for Black-Box Models},
  author={Torfah et al. (2021)},
  year={2021},
  note={arXiv:2108.07307}
}
```

- arXiv: 2108.07307