Can I use this for deep learning models?

Yes, SHAP and LIME are model-agnostic techniques that can be applied to various architectures, including tree-based models and neural networks.

What explanation techniques does this skill use?

It primarily utilizes SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide both local and global model insights.

Is this skill suitable for non-technical stakeholders?

While it uses sophisticated technical methods, the skill is designed to translate those results into clear, concise, and actionable insights for business users.

Do I need to provide the raw model data?

You should provide the relevant model context, input data, and prediction outputs so Claude can accurately calculate the necessary interpretability metrics.

How does this skill help with model fairness?

By identifying which features contribute most to a prediction, you can detect if the model is unfairly relying on biased or sensitive data attributes.

Machine Learning Model Explainability

Name: Machine Learning Model Explainability
Author: BbgnsurfTech

byBbgnsurfTech

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データサイエンスとML

Provides deep interpretability and explainability for machine learning models using advanced techniques like SHAP and LIME.

概要

This skill empowers users to demystify complex machine learning models by providing detailed insights into prediction logic and feature importance. By leveraging industry-standard techniques like SHAP and LIME, it helps developers and data scientists debug model behavior, ensure algorithmic fairness, and communicate technical findings to non-technical stakeholders effectively. Whether you are investigating a specific prediction or looking for global model trends, this skill provides the necessary transparency to build trust in AI-driven systems.

主な機能

Generates human-readable explanations for specific model decisions
3 GitHub stars
Supports model debugging and detection of unexpected feature interactions
Identifies and ranks the most influential features driving outcomes
Facilitates clear communication of model insights to stakeholders
Calculates SHAP and LIME values for model predictions

ユースケース

Identifying the primary behavioral drivers of customer churn in predictive analytics
Debugging unexpected model behavior and ensuring transparency in high-stakes AI applications
Explaining the specific reasoning behind automated loan or credit application decisions

概要

主な機能

Generates human-readable explanations for specific model decisions
3 GitHub stars
Supports model debugging and detection of unexpected feature interactions
Identifies and ranks the most influential features driving outcomes
Facilitates clear communication of model insights to stakeholders
Calculates SHAP and LIME values for model predictions

ユースケース

Identifying the primary behavioral drivers of customer churn in predictive analytics
Debugging unexpected model behavior and ensuring transparency in high-stakes AI applications
Explaining the specific reasoning behind automated loan or credit application decisions