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Ritabrata-Chakraborty
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shap

Computes SHAP values to interpret ML model predictions and feature importance.

Updated Jun 6, 2026
Search Tags:#feature importance#explainable AI#SHAP values#model debugging#model interpretation
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill shap-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/shap
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill shap-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides tools to understand machine learning model predictions, analyze feature importance, and diagnose model issues. It empowers users to make data-driven decisions based on explainable AI principles.

Core Features & Use Cases

  • Model Interpretation: Explain model predictions and understand feature contributions.
  • Feature Importance: Visualize feature importance and analyze their impact on model performance.
  • Model Debugging: Identify and fix model issues, such as data leakage or overfitting.
  • Use Case: When a model prediction is incorrect or unexpected, use this Skill to understand which features influenced the prediction and investigate the underlying reasons.

Quick Start

Run the shap skill to compute SHAP values for a given model and data.

Frequently Asked Questions about shap

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

FAQPage Schema
How do I explain machine learning model predictions and understand feature contributions?▼

Model interpretation relies on SHAP values from cooperative game theory to quantify each feature's impact on a prediction. This Skill computes those values to help you understand and explain model behavior for explainable AI workflows.

What is the best way to debug machine learning models when predictions are unexpected?▼

Model debugging uses SHAP values to identify which features drove an incorrect or unexpected prediction. By analyzing feature importance, you can diagnose issues like data leakage or overfitting and investigate the underlying reasons.

Can I visualize feature importance for my machine learning model?▼

Feature importance visualization is a core feature of this Skill. It applies SHAP value analysis to show how individual features impact model performance and behavior, making it easier to diagnose prediction drivers.

Do I need the shap library installed to compute SHAP values?▼

Yes, the shap library is a required dependency. This Skill relies on the library for efficient SHAP value computation to provide model interpretation and feature importance analysis.

When do I need SHAP values for feature engineering?▼

SHAP values are needed when you want to understand feature contributions to model predictions during feature engineering. This Skill applies cooperative game theory to help you analyze their impact on model performance and refine your features.

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