model-interpretability

Community

Explain ML predictions with SHAP and LIME.

Authorinfantesromeroadrian
Version1.0.0
Installs0

System Documentation

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.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: model-interpretability
Download link: https://github.com/infantesromeroadrian/arca-claude-code/archive/main.zip#model-interpretability

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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