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shap

Explain machine learning model predictions using SHAP values and plots.

1|1|Updated Mar 13, 2026
Search Tags:#debugging#machine learning#model interpretability#feature importance#shap#explainable ai
One-click install
npx skills add https://github.com/youyinnn/skills-collection --skill shap-youyinnn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/youyinnn/skills-collection/tree/main/plugins/model-development-and-experiments/skills/shap
Command: npx skills add https://github.com/youyinnn/skills-collection --skill shap-youyinnn

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: Explain predictions of any ML model (tree-based, deep learning, linear, etc.).
  • Feature Importance: Quantify the impact of each feature on model outcomes.
  • Debugging & Validation: Identify biases, data leakage, and unexpected model behavior.
  • Use Case: After training a loan approval model, use this Skill to understand why a specific applicant was denied, identifying which factors (e.g., credit score, income, debt-to-income ratio) contributed most to the decision.

Quick Start

Use the shap skill to generate a beeswarm plot for the SHAP values of 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 model predictions using SHAP values?▼

SHAP values explain machine learning model predictions by quantifying feature attributions for any black-box model. This Skill generates visual plots like waterfall, beeswarm, and scatter plots to visualize both global and local feature importance using the shap library.

What is the best way to debug model bias and identify feature importance?▼

The best way to debug model bias is to calculate feature attributions using SHAP values. This approach identifies data leakage, uncovers unexpected model behavior, and quantifies the impact of each feature on your model outcomes.

Can I use SHAP for model interpretability with deep learning frameworks like TensorFlow and PyTorch?▼

Yes, you can use SHAP for model interpretability with TensorFlow and PyTorch. This Skill supports explaining predictions for tree-based, deep learning, and linear models by leveraging the shap library alongside your installed frameworks.

How do I generate a beeswarm plot for SHAP values?▼

You generate a beeswarm plot for SHAP values by passing your test dataset into the shap library computation. This Skill orchestrates the calculation and visualization to produce beeswarm, scatter, and waterfall plots for your model explanations.

Does this Skill work with XGBoost and LightGBM models?▼

Yes, this Skill works with XGBoost and LightGBM models. It leverages dependencies like xgboost and lightgbm to compute SHAP values and provide feature attributions for these tree-based algorithms.

When do I need SHAP values for explainable AI?▼

You need SHAP values for explainable AI when you must understand why a model made a specific prediction, such as identifying which factors caused a loan applicant to be denied. It solves model interpretability by providing local and global feature attributions.

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