shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
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Installation for Agentic Skill
View all platforms →skilz install Microck/ordinary-claude-skills/shapskilz install Microck/ordinary-claude-skills/shap --agent opencodeskilz install Microck/ordinary-claude-skills/shap --agent codexskilz install Microck/ordinary-claude-skills/shap --agent geminiFirst time? Install Skilz: pip install skilz
Works with 22+ AI coding assistants
Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...
Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/Microck/ordinary-claude-skillscp -r ordinary-claude-skills/skills_all/claude-scientific-skills/scientific-skills/shap ~/.claude/skills/Need detailed installation help? Check our platform-specific guides:
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Agentic Skill Details
- Repository
- ordinary-claude-skills
- Type
- Non-Technical
- Meta-Domain
- general
- Primary Domain
- general
- Sub-Domain
- machine learning model skill
- Market Score
- 27
Browse Category
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