Analysis toolkit using TensorFlow
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.
Core Features
Ready to Use
Quick integration into your workflow with minimal setup
Community Verified
Active open-source community with continuous updates
Completely Free
MIT/Apache licensed for commercial and personal use
Flexible Extension
Customizable and extendable based on your needs
How to Use
2Install to Claude
Place the skill file in Claude's skills directory (usually ~/.claude/skills/）。
3Start Using
Restart Claude or run the reload command to load the skill
Tip: Read the documentation and code carefully before first use to understand functionality and permission requirements
Technical Information
All Skills from open-source community, preserving original authors' copyrights
K-Dense-AI__claude-scientific-skills/scientific-skills/shap/SKILL.mdRelated Skills
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