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Estrella-231
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shap

Generate SHAP-based feature attributions and plots for model predictions.

Updated May 24, 2026
Search Tags:#model interpretability#feature importance#shap#explainable ai#bias analysis#feature attribution#debugging ml models
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill shap-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/shap
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill shap-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SHAP explains why a machine learning model produced a specific prediction by attributing that output to individual input features, helping you diagnose errors, validate behavior, and build trust in results.

Core Features & Use Cases

  • Feature attribution via Shapley values: quantify each feature’s contribution to a prediction relative to a baseline.
  • Global and local interpretability: create dataset-level importance summaries (beeswarm, bar) and instance-level explanations (waterfall, force).
  • Model-agnostic and model-specific support: handle tree-based models, linear models, deep learning models, and black-box models with the appropriate SHAP explainer.
  • Debugging, bias, and comparison workflows: inspect unexpected feature influence, check subgroup patterns, and compare explanation consistency across models.

Quick Start

Ask the skill to explain which features most influenced your model’s prediction for a single row and generate a SHAP waterfall plot.

Frequently Asked Questions about shap

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

FAQPage Schema
How do I explain which features most influenced a machine learning model's prediction?▼

Feature attribution using Shapley values explains model predictions by quantifying each individual feature's contribution relative to a baseline, generating both global summaries and local instance-level explanations to validate behavior.

Can I generate SHAP explanations for tree-based models and deep learning models?▼

Yes, SHAP supports model-specific explainers for tree-based models, linear models, deep learning models, and model-agnostic approaches for black-box predictors, computing feature attribution values using appropriate background data.

What is the best way to visualize per-instance feature contributions for a single prediction?▼

Standard SHAP plots like waterfall and force plots visualize per-instance feature contributions for a single prediction. These local explanations attribute model output to individual input features relative to a baseline.

How do I check bias and debug unexpected feature influence in my ML model?▼

Debugging and bias checks inspect unexpected feature influence and subgroup patterns by comparing explanation consistency. Dataset-level importance summaries like beeswarm and bar plots reveal global feature attribution patterns for validation.

Do I need background data to compute SHAP values for feature importance?▼

Yes, computing SHAP values requires selecting the correct explainer and providing appropriate background or baseline data to accurately attribute model predictions to individual features across global and local contexts.

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