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sklearn-explainability

Analyze scikit-learn model feature importance with SHAP, LIME, and partial dependence plots.

19|2|Updated Jan 31, 2026
Search Tags:#machine learning#feature importance#model validation#scikit-learn#explainability#interpretability
One-click install
npx skills add https://github.com/tondevrel/scientific-agent-skills --skill sklearn-explainability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sklearn-explainability
Source: https://github.com/tondevrel/scientific-agent-skills/tree/main/skills/sklearn-explainability
Command: npx skills add https://github.com/tondevrel/scientific-agent-skills --skill sklearn-explainability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users understand the decision-making process of machine learning models, ensuring scientific validity and identifying potential biases or artifacts.

Core Features & Use Cases

  • Model Interpretability: Provides tools for both global and local explanations of model predictions.
  • Feature Importance: Ranks the impact of features on model outcomes.
  • Diagnostic Tools: Helps in validating model behavior against scientific principles.
  • Use Case: In drug discovery, use this skill to verify that a model predicting compound efficacy relies on chemically meaningful features rather than spurious correlations.

Quick Start

Use the sklearn-explainability skill to analyze feature importance for the trained model on the test dataset.

Frequently Asked Questions about sklearn-explainability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I interpret feature importance in a scikit-learn model?▼

Feature importance in scikit-learn models is interpreted using built-in inspection tools like permutation importance and PartialDependenceDisplay, alongside SHAP and LIME, to rank feature impacts on predictions.

What is the best way to explain machine learning model predictions for scientific validation?▼

Explaining machine learning predictions for scientific validation involves using global and local feature importance analysis to verify model behavior against scientific principles and identify spurious correlations.

Can I use SHAP and LIME for model-agnostic explainability with scikit-learn?▼

Yes, SHAP and LIME are integrated for model-agnostic explanations with scikit-learn, providing both global and local interpretations of model predictions to uncover decision-making processes.

How do I generate partial dependence plots for a trained classifier?▼

Partial dependence plots for a trained classifier are generated using scikit-learn's PartialDependenceDisplay tool, which visualizes the marginal effect of features on model outcomes for diagnostic validation.

When do I need local vs global interpretability for machine learning models?▼

Global interpretability explains overall feature importance across the dataset, while local interpretability explains individual predictions; both are needed to identify potential biases and ensure scientific validity.

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