Can I use this for non-technical stakeholders?

Yes, the skill is specifically designed to translate complex feature interactions into clear, concise summaries suitable for business presentations and stakeholder communication.

Does it work with all machine learning models?

The skill supports a wide range of architectures, including tree-based models and neural networks, by automatically selecting the most appropriate interpretability technique for the specific model type.

Which explanation techniques does this skill support?

This skill primarily utilizes SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide robust, model-agnostic insights.

How does this help with model fairness and transparency?

By identifying which features most influence a decision, users can detect if the model is relying on biased or inappropriate variables, ensuring higher standards of transparency and ethics.

ML Model Explainability

Name: ML Model Explainability
Author: jeremylongshore

byjeremylongshore

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883

データサイエンスとML

Provides deep insights into machine learning model predictions and behavior using interpretability techniques like SHAP and LIME.

概要

This skill empowers Claude to demystify black-box machine learning models by providing clear, actionable explanations for individual predictions and overall model logic. By leveraging industry-standard techniques such as SHAP and LIME, the skill helps data scientists and developers identify influential features, debug unexpected model performance, and ensure algorithmic fairness. It is particularly effective for communicating complex technical insights to non-technical stakeholders and ensuring transparency in automated decision-making processes.

主な機能

Support for various model architectures including tree-based and neural networks
Diagnostic tools for debugging unexpected feature interactions
SHAP and LIME integration for local and global model explanations
Automated identification and ranking of feature importance
Stakeholder-friendly summaries of complex model behavior
883 GitHub stars

ユースケース

Identifying and mitigating bias by auditing feature contributions to model outcomes
Generating interpretability reports to communicate model insights to business leaders
Explaining the specific factors behind individual model predictions like loan rejections

概要

主な機能

Support for various model architectures including tree-based and neural networks
Diagnostic tools for debugging unexpected feature interactions
SHAP and LIME integration for local and global model explanations
Automated identification and ranking of feature importance
Stakeholder-friendly summaries of complex model behavior
883 GitHub stars

ユースケース

Identifying and mitigating bias by auditing feature contributions to model outcomes
Generating interpretability reports to communicate model insights to business leaders
Explaining the specific factors behind individual model predictions like loan rejections