Explain ML Model

You are generating explainability artifacts for an ML model in a SpecWeave increment. Make the black box transparent.

Your Task

1. Load Model: Load model from increment
2. Generate Global Explanations: Feature importance, partial dependence
3. Generate Local Explanations: SHAP/LIME for sample predictions
4. Create Report: Comprehensive explainability documentation

Explainability Steps

Step 1: Feature Importance

from specweave import ModelExplainer

explainer = ModelExplainer(model, X_train)
importance = explainer.feature_importance()

Create: feature-importance.png

Step 2: SHAP Summary

shap_values = explainer.shap_summary()

Create: shap-summary.png (beeswarm plot)

Step 3: Partial Dependence Plots

for feature in top_features:
    pdp = explainer.partial_dependence(feature)

Create: pdp-plots/ directory

Step 4: Local Explanations

# Explain sample predictions
samples = [high_confidence, low_confidence, edge_case]
for sample in samples:
    explanation = explainer.explain_prediction(sample)

Create: local-explanations/ directory

Step 5: Generate Report

Create explainability-report.md:

# Model Explainability Report

## Global Feature Importance
[Top 10 features with importance scores]

## SHAP Analysis
[Summary plot and interpretation]

## Partial Dependence
[How each feature affects predictions]

## Example Explanations
[3-5 example predictions with full explanations]

## Recommendations
[Model improvements based on feature analysis]

Output

Report:

• Top 10 most important features
• Any surprising feature importance (might indicate data leakage)
• Model behavior insights
• Recommendations for improvement