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shap

Compute Shapley values to attribute feature importance for machine learning predictions.

7|1|Updated Jan 14, 2026
Search Tags:#data science#machine learning#feature importance#interpretability#shap#model explainability
One-click install
npx skills add https://github.com/eyadsibai/ltk --skill shap-eyadsibai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/eyadsibai/ltk/tree/main/plugins/ltk-data/skills/shap
Command: npx skills add https://github.com/eyadsibai/ltk --skill shap-eyadsibai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, xgboost, tensorflow, pytorch, scikit-learn, and includes references (resource) components.

What problem does it solve?

This Skill demystifies complex machine learning models by explaining individual predictions and overall feature importance, making models more transparent and trustworthy.

Core Features & Use Cases

  • Feature Attribution: Understand how each feature contributes to a specific prediction.
  • Model Explainability: Debug model behavior, validate fairness, and build trust in AI systems.
  • Interpretability Plots: Generate visualizations like waterfall, beeswarm, and bar plots for clear insights.
  • Use Case: After training a loan approval model, use this Skill to explain why a specific applicant was denied, highlighting the key factors (e.g., credit score, income) that influenced the decision.

Quick Start

Use the shap skill to compute and visualize feature importance for your trained XGBoost model on the test dataset.

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 predictions using Shapley values?▼

Model explainability using Shapley values provides both global and local interpretability, calculating feature attribution to show exactly how specific features influence individual predictions and overall model behavior.

Can I use Shapley values with XGBoost, TensorFlow, and PyTorch models?▼

Yes, Shapley value model explainability supports various model types including tree-based models like XGBoost, deep learning frameworks like TensorFlow and PyTorch, and linear models for feature importance attribution.

What's the best way to visualize feature importance for a trained scikit-learn model?▼

The best way to visualize feature importance is by generating interpretability plots such as waterfall, beeswarm, and bar plots, which provide clear insights into how features contribute to your scikit-learn model predictions.

How do I debug model fairness and validate feature attribution for loan approvals?▼

Debug model fairness and validate feature attribution by analyzing Shapley values to understand why a specific applicant was denied, highlighting key factors like credit score and income that influenced the machine learning decision.

Do I need the shap library to generate global interpretability plots for machine learning?▼

Yes, you need the shap library to compute Shapley values and generate both global and local interpretability plots for visualizing feature importance and explaining machine learning model predictions.

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