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model-interpretability

Explains model predictions using SHAP, LIME, attention visualization, and fairness auditing.

Updated Aug 27, 2026
Search Tags:#fairness#explanation#attention#counterfactual#interpretability#shap#lime
One-click install
npx skills add https://github.com/infantesromeroadrian/arca-claude-code --skill model-interpretability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-interpretability
Source: https://github.com/infantesromeroadrian/arca-claude-code/tree/main/skills/model-interpretability
Command: npx skills add https://github.com/infantesromeroadrian/arca-claude-code --skill model-interpretability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provide clear, actionable explanations for ML model predictions to support debugging, regulatory compliance, and stakeholder communication.

Core Features & Use Cases

  • Comprehensive overview of methods (SHAP, LIME, attention visualization, and fairness auditing) and when to use them.
  • Guidance for debugging predictions and validating model behavior with per-instance and global explanations.
  • Practical examples for compliance reporting, model cards, and communicating results to non-technical audiences.

Quick Start

Explain a model's prediction for a given instance using SHAP or LIME and generate a concise, human-readable explanation.

Frequently Asked Questions about model-interpretability

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

FAQPage Schema
How do I explain ML model predictions using SHAP and LIME?▼

You can explain ML model predictions by applying SHAP and LIME techniques to generate per-instance and global explanations. This provides clear, actionable insights for debugging model behavior and validating predictions.

What is the difference between SHAP and LIME for model interpretability?▼

SHAP and LIME are both model interpretability methods used to explain predictions. SHAP provides unified feature importance based on game theory, while LIME offers local approximations, and choosing between them depends on specific debugging or compliance needs.

How do I generate fairness audits and attention visualizations for machine learning models?▼

Fairness audits and attention visualizations are generated as part of comprehensive model interpretability checks. These techniques help validate model behavior, identify biases, and ensure reproducible explanations for regulatory compliance.

Can I use interpretability techniques for regulatory compliance and stakeholder communication?▼

Yes, interpretability techniques support regulatory compliance and stakeholder communication by providing reproducible explanations with validation. They generate practical examples for compliance reporting and model cards tailored for non-technical audiences.

When do I need counterfactual explanations for my ML model?▼

Counterfactual explanations are needed when debugging predictions or auditing fairness to understand how minimal feature changes impact outcomes. They provide actionable insights for validating model behavior and ensuring reliable regulatory compliance.

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