Does this skill support SHAP and LIME integrations?

Yes, it includes patterns for integrating SHAP and other model-agnostic tools to explain predictions for any scikit-learn compatible estimator.

Is this skill useful for non-linear model interpretation?

Yes, it covers Partial Dependence Plots and SHAP values, which are specifically designed to reveal how non-linear models handle feature interactions.

Can this skill help identify data leakage?

Absolutely. It provides workflows to detect 'leakage' features—variables that show suspiciously high importance and likely contain information not available at prediction time.

Why use Permutation Importance instead of the default tree importance?

Default feature importance in models like Random Forest is often biased toward high-cardinality features; this skill teaches you to use Permutation Importance for more reliable results.

Scikit-Learn Model Explainability

Name: Scikit-Learn Model Explainability
Author: tondevrel

bytondevrel

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Data Science & ML

Provides specialized guidance and code patterns for interpreting machine learning models using scikit-learn, SHAP, and advanced diagnostic tools.

This skill transforms scikit-learn from a 'black box' into a transparent scientific tool by providing best practices for model interpretability and validation. It guides developers through implementing global and local explanations, utilizing model-agnostic techniques like SHAP and Permutation Importance, and generating Partial Dependence Plots to visualize feature relationships. Whether you are auditing for regulatory compliance or validating scientific hypotheses, this skill ensures your models are making decisions based on meaningful features rather than data artifacts or leakage.

Key Features

01Partial Dependence Plots (PDP) for visualizing feature-target relationships

021 GitHub stars

03Permutation Importance workflows to avoid high-cardinality bias

04Global and local interpretability patterns for individual and system-wide insights

05Implementation of SHAP and LIME for model-agnostic explanations

06Diagnostic tools for detecting data leakage and model instability

Use Cases

01Validating scientific models to ensure predictions align with physical laws

02Auditing machine learning models for regulatory compliance and bias detection

03Explaining individual model predictions to non-technical stakeholders and users

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tondevrel/scientific-agent-skills sklearn-explainability

For use in Claude.ai and ChatGPT

Download Skill