datarobot-model-explainabilitylisted

# DataRobot Model Explainability Skill This skill covers SHAP insights, XEMP prediction explanations, anomaly explanations, and model diagnostics. > **SDK version**: Use `datarobot>=3.6.0` for the full API set in this skill (`ShapDistributions` > was added in 3.6; `ShapMatrix`, `ShapImpact`, and `ShapPreview` are available in > `datarobot>=3.4.0`). Use `from datarobot.insights import ShapMatrix, ...` with > `entity_id=model_id` — not legacy `datarobot.models.ShapMatrix` (`project_id` / `dataset_id`). > `ShapMatrix`, `ShapImpact`, `ShapPreview`, and `ShapDistributions` are the canonical SHAP API. > The older `dr.PredictionExplanations` (XEMP-based) remains available but is the secondary path. --- ## Quick Start | Goal | API to use | Prerequisites | |------|-----------|---------------| | SHAP values for all features, all rows | `ShapMatrix.create(entity_id=model_id)` | None - universal SHAP | | Per-row top-feature explanations | `ShapPreview.create(entity_id=model_id)` | None | | Aggregated feature importance via SHAP | `ShapImpact.create(entity_id=model_id)` | None | | SHAP value distributions across features | `ShapDistributions.create(entity_id=model_id)` | None | | SHAP for a filtered segment | `dr.DataSlice.create(...)` + `ShapMatrix.create(..., data_slice_id=...)` | Data slice definition | | XEMP-based prediction explanations | `dr.PredictionExplanations.create(...)` | Feature Impact; PE initialization; dataset uploaded | | Anomaly explanations (time series) | `Anoma