shap-model-explainability
Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.
npx skills add BioTender-max/awesome-bio-agent-skills --skill shap-model-explainability --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SHAP Model Explainability ## Overview SHAP (SHapley Additive exPlanations) is a unified framework for explaining machine learning model predictions using Shapley values from cooperative game theory. It quantifies each feature's contribution to individual predictions and provides both local (per-instance) and global (dataset-level) explanations with theoretical guarantees of consistency and additivity. ## When to Use - Explaining which features drive a model's predictions (global importance) - Understanding why a model made a specific prediction (local explanation) - Debugging model behavior and identifying data leakage - Analyzing model fairness across demographic groups - Comparing feature importance across multiple models - Generating interpretable model explanations for stakeholders - For tree-based model interpretation, prefer SHAP over permutation importance or Gini importance (more accurate, instance-level) - For deep learning interpretation on images, consider GradCAM; use SHAP for tabular/structured data ## Prerequisites ```bash pip install shap matplotlib # Optional: xgboost lightgbm tensorflow torch (depending on model) ``` ## Quick Start ```python import shap import xg
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Select the Right Explainer
- Step 2: Compute SHAP Values
- Step 3: Global Explanations
- Step 4: Local Explanations (Individual Predictions)
- Step 5: Feature Relationships
- Step 6: Advanced Visualizations
- Key Parameters
- Key Concepts
- SHAP Value Properties
pip install shap matplotlib
What does the shap-model-explainability skill do?
Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill shap-model-explainability --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.