model-explainer

name	model-explainer
description	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.

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:

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:

# How does feature affect prediction?
explainer.partial_dependence(feature=)