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shap

Explain machine learning model predictions and feature importance using SHAP values.

Updated Jan 10, 2026
Search Tags:#machine learning#model interpretability#feature importance#shap#explainable ai#prediction explanation
One-click install
npx skills add https://github.com/robinbarvaag/poynt --skill shap-robinbarvaag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/shap
Command: npx skills add https://github.com/robinbarvaag/poynt --skill shap-robinbarvaag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, matplotlib, numpy, pandas, scikit-learn, xgboost, lightgbm, tensorflow, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides deep insights into how machine learning models make predictions, enabling users to understand feature importance, debug models, and ensure fairness.

Core Features & Use Cases

  • Model Interpretability: Understand which features drive model predictions.
  • Feature Importance: Quantify the impact of each feature on model outcomes.
  • Prediction Debugging: Analyze why a specific prediction was made.
  • Fairness Analysis: Detect and mitigate bias in model behavior.
  • Use Case: A data scientist can use this Skill to explain why a loan application was denied by identifying the key features that contributed to the negative prediction.

Quick Start

Use the shap skill to generate a beeswarm plot for the model's SHAP values.

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 feature importance?▼

You explain machine learning model predictions by computing SHAP values to quantify the impact of each feature on model outcomes. This enables detailed analysis of individual predictions, global feature impacts, and model fairness.

Can I use SHAP values with tree-based, deep learning, and linear models?▼

Yes, SHAP values support various model types including tree-based, deep learning, and linear models. The implementation integrates directly with scikit-learn, xgboost, lightgbm, tensorflow, and torch frameworks.

How do I debug why a specific prediction was made in my ML model?▼

You debug a specific prediction by generating SHAP values to analyze the exact feature contributions driving that individual model outcome. This process identifies the key features responsible for specific predictions.

What is the best way to detect bias and ensure fairness in model behavior?▼

To detect bias and ensure fairness in model behavior, you analyze SHAP values to identify disproportionate feature impacts across different groups. This highlights features contributing to biased model predictions.

Do I need pandas and matplotlib to visualize model interpretability results?▼

Yes, you need pandas for data manipulation and matplotlib to generate visualizations like beeswarm plots for SHAP values. The computation also relies on numpy for processing the underlying data arrays.

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