ml-model-explainer
Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.
npx skills add majiayu000/claude-skill-registry --skill ml-model-explainer-dkyazzentwatwa-chatgpt-skills --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.
# ML Model Explainer Explain machine learning model predictions using SHAP and feature importance. ## Features - **SHAP Values**: Explain individual predictions - **Feature Importance**: Global feature rankings - **Decision Paths**: Trace prediction logic - **Visualizations**: Waterfall, force plots, summary plots - **Multiple Models**: Support for tree-based, linear, neural networks - **Batch Explanations**: Explain multiple predictions ## Quick Start ```python from ml_model_explainer import MLModelExplainer explainer = MLModelExplainer() explainer.load_model(model, X_train) # Explain single prediction explanation = explainer.explain(X_test[0]) explainer.plot_waterfall('explanation.png') # Feature importance importance = explainer.feature_importance() ``` ## CLI Usage ```bash python ml_model_explainer.py --model model.pkl --data test.csv --output explanations/ ``` ## Dependencies - shap>=0.42.0 - scikit-learn>=1.3.0 - pandas>=2.0.0 - numpy>=1.24.0 - matplotlib>=3.7.0
- Features
- Quick Start
- CLI Usage
- Dependencies
python ml_model_explainer.py --model model.pkl --data test.csv --output explanations/
What does the ml-model-explainer skill do?
Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-model-explainer-dkyazzentwatwa-chatgpt-skills --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 majiayu000/claude-skill-registry, a repository with 534 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.