xailib.xailib_transparent_by_design

Transparent-by-design model classes for XAI-Lib.

This module provides base classes for inherently interpretable models that are transparent by design. Unlike post-hoc explanation methods, these models provide built-in interpretability without requiring additional explanation techniques.

Examples of transparent-by-design models include:

• Neural Additive Models (NAM)

• Generalized Additive Models (GAM)

• Decision trees and rule-based models

• Linear models with interpretable features

Classes:

Explainer: Base class for transparent models (extended with predict methods). Explanation: Base class for transparent model explanations.

Example

Using a transparent-by-design model:

from xailib.explainers.nam_explainer_tab import NAMExplainer

# Create and fit the transparent model
explainer = NAMExplainer()
explainer.fit(X_train, y_train)

# Get predictions with built-in explanations
prediction = explainer.predict(X_test)
explanation = explainer.explain(X_test[0])

Note

Models in this module can both make predictions AND provide explanations, unlike post-hoc explainers that only explain existing black-box models.

Classes

Explainer()	Abstract base class for transparent-by-design models.
Explanation()	Abstract base class for transparent model explanations.