What types of models can Claude analyze with this skill?

The skill is designed to handle various model types, including tree-based models (XGBoost, Random Forest), neural networks, and linear regressions, selecting the most appropriate technique for the model type.

Does this skill provide visualizations?

It generates structured data and detailed textual descriptions of feature importance that can be paired with data visualization tools for a comprehensive model analysis workflow.

Can I use this for regulatory compliance and fairness?

Yes, it provides the transparency needed to explain automated decisions, which is critical for identifying bias and ensuring compliance in regulated fields like finance and healthcare.

How does this skill explain model predictions?

It uses standard interpretability frameworks like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to quantify the contribution of each input feature toward the final prediction.

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 SHAP and LIME techniques.

概要

This skill empowers Claude to analyze machine learning models, offering clear insights into why specific predictions are made and identifying the most influential features driving model outputs. By leveraging advanced interpretability techniques like SHAP and LIME, it helps developers and data scientists debug model performance, ensure algorithmic fairness, and communicate complex insights to non-technical stakeholders in a clear, actionable format.

主な機能

Feature importance ranking and analysis
Model debugging and interaction identification
Automated SHAP and LIME value calculation
Local explanation generation for specific predictions
3 GitHub stars
Stakeholder-friendly insight summaries

ユースケース

Identifying global feature importance to improve model transparency and fairness
Explaining individual model predictions for high-stakes decisions like loan approvals
Debugging unexpected model behavior by analyzing complex feature interactions

概要

主な機能

Feature importance ranking and analysis
Model debugging and interaction identification
Automated SHAP and LIME value calculation
Local explanation generation for specific predictions
3 GitHub stars
Stakeholder-friendly insight summaries

ユースケース

Identifying global feature importance to improve model transparency and fairness
Explaining individual model predictions for high-stakes decisions like loan approvals
Debugging unexpected model behavior by analyzing complex feature interactions