ML Model Explanation
This is a prompt designed to help Claude explain how machine learning models make their predictions. You'd use it when you need to understand feature importance, generate SHAP values, create LIME explanations, or visualize what your model is actually looking at. It covers both global model behavior and local per-prediction breakdowns. The skill documentation is pretty thorough on explainability techniques, which is good because there's real nuance between something like partial dependence plots versus attention maps. Useful if you're debugging a model, need to satisfy compliance requirements, or just want to know why your neural network thinks that image is a cat.
- GitHub stars
- 317
- last updated
- Aug 21, 2026
Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill 'ML Model Explanation' --agent claude-codeInstalls into .claude/skills of the current project.
Files
ML Model Explanation
Model explainability makes machine learning decisions transparent and interpretable, enabling trust, compliance, debugging, and actionable insights from predictions.
Explanation Techniques
- Feature Importance: Global feature contribution to predictions
- SHAP Values: Game theory-based feature attribution
- LIME: Local linear approximations for individual predictions
- Partial Dependence Plots: Feature relationship with predictions
- Attention Maps: Visualization of model focus areas
- Surrogate Models: Simpler interpretable approximations
Explainability Types
- Global: Overall model behavior and patterns
- Local: Explanation for individual predictions
- Feature-Level: Which features matter most
- Model-Level: How different components interact
Python Implementation
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- Repository
- github.com/aj-geddes/useful-ai-prompts
- Categories
- AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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