explaining-machine-learning-models

Solid

This skill enables Claude to provide interpretability and explainability for machine learning models. It is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior. The skill leverages techniques like SHAP and LIME to generate explanations. It is useful when debugging model performance, ensuring fairness, or communicating model insights to stakeholders. Use this skill when the user mentions "explain model", "interpret model", "feature importance", "SHAP values", or "LIME explanations".

AI & Automation 2,266 stars 315 forks Updated today MIT

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Skill Content

## Overview This skill empowers Claude to analyze and explain machine learning models. It helps users understand why a model makes certain predictions, identify the most influential features, and gain insights into the model's overall behavior. ## How It Works 1. **Analyze Context**: Claude analyzes the user's request and the available model data. 2. **Select Explanation Technique**: Claude chooses the most appropriate explanation technique (e.g., SHAP, LIME) based on the model type and the user's needs. 3. **Generate Explanations**: Claude uses the selected technique to generate explanations for model predictions. 4. **Present Results**: Claude presents the explanations in a clear and concise format, highlighting key insights and feature importances. ## When to Use This Skill This skill activates when you need to: - Understand why a machine learning model made a specific prediction. - Identify the most important features influencing a model's output. - Debug model performance issues by identifying unexpected feature interactions. - Communicate model insights to non-technical stakeholders. - Ensure fairness and transparency in model predictions. ## Examples ### Example 1: Understanding Loan Application Decisions User request: "Explain why this loan application was rejected." The skill will: 1. Analyze the loan application data and the model's prediction. 2. Calculate SHAP values to determine the contribution of each feature to the rejection decision. 3. Present the r...

Details

Author: jeremylongshore
Repository: jeremylongshore/claude-code-plugins-plus-skills
Created: 7 months ago
Last Updated: today
Language: Python
License: MIT

Integrates with

Anthropic · AI

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