model-explainer
Model interpretability and explainability using SHAP, LIME, feature importance, and partial dependence plots. Activates for "explain model", "model interpretability", "SHAP", "LIME", "feature importance", "why prediction", "model explanation". Generates human-readable explanations for model predictions, critical for trust, debugging, and regulatory compliance.
npx skills add majiayu000/claude-skill-registry --skill model-explainer --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.
# Model Explainer ## Overview Makes black-box models interpretable. Explains why models make specific predictions, which features matter most, and how features interact. Critical for trust, debugging, and regulatory compliance. ## Why Explainability Matters - **Trust**: Stakeholders trust models they understand - **Debugging**: Find model weaknesses and biases - **Compliance**: GDPR, fair lending laws require explanations - **Improvement**: Understand what to improve - **Safety**: Detect when model might fail ## Explanation Types ### 1. Global Explanations (Model-Level) **Feature Importance**: ```python from specweave import explain_model explainer = explain_model( model=trained_model, X_train=X_train, increment="0042" ) # Global feature importance importance = explainer.feature_importance() ``` Output: ``` Top Features (Global): 1. transaction_amount (importance: 0.35) 2. user_history_days (importance: 0.22) 3. merchant_reputation (importance: 0.18) 4. time_since_last_transaction (importance: 0.15) 5. device_type (importance: 0.10) ``` **Partial Dependence Plots**: ```python # How does feature affect prediction? explainer.partial_dependence(feature="transaction_amount") ``` ### 2.
- Overview
- Why Explainability Matters
- Explanation Types
- 1. Global Explanations (Model-Level)
- 2. Local Explanations (Prediction-Level)
- Usage in SpecWeave
- Real-World Examples
- Example 1: Fraud Detection
- Example 2: Loan Approval
- Regulatory Compliance
- GDPR "Right to Explanation"
- Fair Lending Act
- Visualization Types
- Integration with SpecWeave
Generate all explainability artifacts Explain specific prediction Check for bias
What does the model-explainer skill do?
Model interpretability and explainability using SHAP, LIME, feature importance, and partial dependence plots. Activates for "explain model", "model interpretability", "SHAP", "LIME", "feature importance", "why prediction", "model explanation". Generates human-readable explanations for model predictions, critical for trust, debugging, and regulatory compliance.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-explainer --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.