How does this skill calculate feature importance?

It leverages techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to quantify how much each input feature contributes to the final prediction.

Is data preprocessing required?

For accurate explanations, the data provided to the skill should be preprocessed and aligned with the format the model expects for inference.

What machine learning models are supported?

This skill is designed to work with various model architectures, including tree-based models (like XGBoost and Random Forest) and neural networks, provided the necessary data context is available.

Can this skill help identify model bias?

Yes, by revealing which features most heavily influence a model's decisions, it allows developers to detect if a model is relying on sensitive or inappropriate attributes.

Machine Learning Model Explainability

Name: Machine Learning Model Explainability
Author: jeremylongshore

byjeremylongshore

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883

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Data Science & ML

Provides deep interpretability and transparency for machine learning models using industry-standard techniques like SHAP and LIME.

This skill empowers Claude to demystify complex machine learning models by providing clear, actionable insights into model behavior and prediction logic. By leveraging interpretability frameworks such as SHAP and LIME, it allows users to identify the most influential features, understand individual prediction outcomes, and debug performance issues related to unexpected feature interactions. This is an essential tool for data scientists and developers who need to ensure model fairness, communicate findings to stakeholders, and maintain transparency in automated decision-making systems.

Key Features

01Individual prediction breakdown for decision transparency

02883 GitHub stars

03Support for tree-based models and neural networks

04Local and global model explanations via SHAP and LIME

05Stakeholder-ready summaries of complex model behavior

06Detailed feature importance analysis and ranking

Use Cases

01Ensuring fairness and transparency in high-stakes automated decisions

02Communicating predictive drivers to non-technical business stakeholders

03Debugging unexpected model outputs or performance regressions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills skill-adapter

For use in Claude.ai and ChatGPT

Download Skill