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shap

Explain machine learning model predictions using SHAP values.

Updated Aug 23, 2026
Search Tags:#data science#machine learning#model interpretability#feature importance#shap#explainable ai
One-click install
npx skills add https://github.com/jacketlong23/skills --skill shap-jacketlong23
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/jacketlong23/skills/tree/main/shap
Command: npx skills add https://github.com/jacketlong23/skills --skill shap-jacketlong23

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, helping users understand feature importance, debug models, and ensure fairness.

Core Features & Use Cases

  • Feature Importance: Identify which features most influence model predictions.
  • Local Explanations: Understand why a specific prediction was made for an individual data point.
  • Model Debugging: Detect biases, data leakage, or unexpected model behavior.
  • Use Case: After training a credit risk model, use this Skill to explain why a particular applicant was denied, highlighting the key factors that led to the decision.

Quick Start

Use the shap skill to generate a beeswarm plot for the model explanations.

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 importance?▼

Explain machine learning model predictions by calculating SHAP values to quantify feature importance and attribute predictions. This approach solves model interpretability problems by highlighting how specific features drive individual predictions and overall model behavior.

Can I use SHAP explainers with deep learning frameworks like TensorFlow and PyTorch?▼

Yes, SHAP explainers support deep learning frameworks like TensorFlow and PyTorch. The implementation also handles tree-based models using XGBoost and LightGBM, as well as linear models from scikit-learn, providing interpretability across diverse model architectures.

How do I generate a beeswarm plot for model explanations?▼

Generate a beeswarm plot for model explanations by passing your trained model and dataset to the SHAP explainer. The Skill uses matplotlib to visualize feature attribution, displaying the distribution of SHAP values to show each feature's impact on predictions.

What's the best way to debug machine learning models for bias and data leakage?▼

Debug machine learning models for bias and data leakage by analyzing SHAP values to detect unexpected feature attributions. This method reveals whether sensitive features disproportionately influence predictions or if anomalous attributions indicate data leakage.

Why does my credit risk model deny specific applicants and how can I visualize the key factors?▼

Understand why a credit risk model denies specific applicants by using local SHAP explanations to visualize the key factors leading to the decision. This isolates individual feature contributions, clarifying exactly which variables drove the negative outcome for that applicant.

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