# Ce Integration Compare

> Guide CE integration with SHAP and LIME, compare explanation outputs, and configure the calibrated-explanations integration adapters.

- Skill: `majiayu000/ce-integration-compare` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/ce-integration-compare`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/ce-integration-compare/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/ce-integration-compare

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# CE Integration Compare

You are helping users work with the CE integrations for SHAP and LIME.

## Required references

- `src/calibrated_explanations/integrations/shap.py`
- `src/calibrated_explanations/integrations/lime.py`
- `src/calibrated_explanations/integrations/__init__.py`

## Use this skill when

- A user asks how to use CE with SHAP or LIME.
- Comparing CE explanations with SHAP/LIME outputs.
- Configuring or debugging the integration adapters.
- Understanding the differences between calibrated explanations and
  feature-attribution methods.

## Key concepts

### CE vs. feature attribution methods

Calibrated explanations provide **rule-based explanations with calibrated
uncertainty intervals**. SHAP and LIME provide **feature attribution values**
(importance scores per feature). The approaches are complementary:

- CE rules tell you *which conditions* justify a prediction with calibrated
  confidence.
- SHAP/LIME tell you *how much* each feature contributed to the prediction.

### Integration architecture

The integration modules provide adapter classes that:
1. Wrap a fitted CE explainer to produce SHAP-compatible or LIME-compatible
   outputs.
2. Allow side-by-side comparison of CE rules with feature attributions.
3. Preserve CE calibration semantics while exposing familiar interfaces.

## Workflow

1. **Fit and calibrate a CE pipeline first** (use `ce-pipeline-builder`).
2. **Import the integration adapter**:
   ```python
   from calibrated_explanations.integrations.shap import CESHAPAdapter
   # or
   from calibrated_explanations.integrations.lime import CELIMEAdapter
   ```
3. **Generate comparison explanations** using both CE and the adapter.
4. **Interpret the results**: CE rules for conditions, SHAP/LIME values for
   attribution magnitudes.

## Constraints

- Always fit and calibrate the CE pipeline before using integration adapters.
- Integration adapters are convenience wrappers; the underlying CE explainer
  must be properly configured.
- Do not use integration adapters as replacements for CE explanations; they
  are comparison tools.