Can I use this for non-technical reports?

Yes, the skill is specifically designed to translate complex mathematical weights and feature contributions into human-readable insights for business stakeholders.

How does this skill help with model debugging?

It identifies exactly which features are driving specific predictions, allowing you to spot unexpected correlations, data leakage, or model errors.

Does this work with any machine learning model?

It is designed to work with most standard Python-based ML frameworks, particularly those compatible with SHAP and LIME, such as Scikit-Learn, XGBoost, and LightGBM.

Which interpretability techniques are supported?

The skill primarily leverages SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide robust results.

ML Model Explainability & Interpretation

Name: ML Model Explainability & Interpretation
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

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

概要

The ML Model Explainability skill empowers Claude to demystify complex machine learning models by providing clear, actionable insights into their decision-making processes. By leveraging industry-standard techniques like SHAP and LIME, the skill identifies the most influential features behind specific predictions or overall model behavior. This is essential for developers and data scientists who need to debug model performance, ensure algorithmic fairness, and communicate technical results to non-technical stakeholders effectively.

主な機能

Stakeholder-friendly summaries of complex AI decisions
Local prediction interpretation for individual data points
Detailed feature importance analysis for global insights
0 GitHub stars
Automated SHAP and LIME explanation generation
Model performance debugging through feature interaction analysis

ユースケース

Explaining the logic behind individual credit or loan approval decisions
Auditing machine learning models for bias and ensuring transparency
Identifying the key drivers of customer churn in a classification model

概要

主な機能

Stakeholder-friendly summaries of complex AI decisions
Local prediction interpretation for individual data points
Detailed feature importance analysis for global insights
0 GitHub stars
Automated SHAP and LIME explanation generation
Model performance debugging through feature interaction analysis

ユースケース

Explaining the logic behind individual credit or loan approval decisions
Auditing machine learning models for bias and ensuring transparency
Identifying the key drivers of customer churn in a classification model