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datarobot-model-explainability

Compute SHAP matrices and XEMP prediction explanations for DataRobot models.

24|22|Updated Dec 14, 2025
Search Tags:#diagnostics#shap#model explainability#xemp#anomaly assessment#data slices#roc lift confusion
One-click install
npx skills add https://github.com/datarobot-oss/datarobot-agent-skills --skill datarobot-model-explainability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datarobot-model-explainability
Source: https://github.com/datarobot-oss/datarobot-agent-skills/tree/main/skills/datarobot-model-explainability
Command: npx skills add https://github.com/datarobot-oss/datarobot-agent-skills --skill datarobot-model-explainability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

It helps you understand why a DataRobot model makes certain predictions by computing SHAP-based insights, XEMP prediction explanations, anomaly explanation artifacts, and common diagnostics like ROC/lift/confusion.

Core Features & Use Cases

  • SHAP explainability (primary path): Generate full-row SHAP matrices, per-row top-feature previews, aggregated feature importance, and SHAP distributions, optionally filtered with Data Slices.
  • XEMP prediction explanations (secondary path): Produce XEMP-based per-row explanations when SHAP is unavailable or when XEMP is specifically required, following required prerequisites like Feature Impact computation and initialization.
  • Model diagnostics and anomaly explanations: Retrieve ROC, lift, and confusion insights and compute time-series anomaly assessment explanations using AnomalyAssessmentRecord.

Quick Start

Use the datarobot-model-explainability skill to compute SHAP values for all features and all rows for a given model by asking for a ShapMatrix with entity_id set to the model ID.

Frequently Asked Questions about datarobot-model-explainability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute SHAP values for DataRobot model predictions?▼

You can compute SHAP values for DataRobot predictions by requesting a ShapMatrix using datarobot.insights SHAP APIs with entity_id set to your model ID, generating full-row matrices and aggregated feature importance.

What is the difference between SHAP and XEMP prediction explanations in DataRobot?▼

SHAP provides direct feature impact insights using datarobot.insights APIs, whereas XEMP prediction explanations are a secondary path requiring Feature Impact computation and PredictionExplanationsInitialization prerequisites before generating per-row explanations via dr.PredictionExplanations.

Can I filter SHAP insights by specific data segments in DataRobot?▼

Yes, you can filter SHAP insights by specific data segments using optional dr.DataSlice filtering when generating explainability artifacts for your DataRobot models.

Do I need to run Feature Impact before generating XEMP prediction explanations?▼

Yes, generating XEMP prediction explanations via dr.PredictionExplanations requires completing Feature Impact computation and PredictionExplanationsInitialization prerequisites first.

How do I retrieve ROC, lift, and confusion diagnostics for DataRobot models?▼

You retrieve ROC, lift, and confusion diagnostics to evaluate model performance by requesting diagnostic insights artifacts through the DataRobot explainability workflow.

How do I compute time-series anomaly explanations in DataRobot?▼

You compute time-series anomaly assessment explanations using AnomalyAssessmentRecord to diagnose anomaly behavior and generate anomaly explanation outputs for your DataRobot models.

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