Reproduce “InterpretML: A Unified Framework for Machine Learning Interpretability” on OpenResearch

About this paper

Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana

Microsoft researchers developed InterpretML, an open-source Python framework that unifies various machine learning interpretability algorithms, providing a consistent API and visualization platform for comparison. The framework also introduces the Explainable Boosting Machine (EBM), a novel intrinsically interpretable "glassbox" model, which demonstrates predictive accuracy comparable to complex blackbox models across diverse datasets and offers significantly faster prediction times.

Key insights

• A unified framework and consistent API can significantly streamline the process of comparing, evaluating, and adopting diverse machine learning interpretability techniques.
• Intrinsically interpretable models, like EBM, can achieve predictive accuracy competitive with state-of-the-art blackbox models, challenging the traditional accuracy-interpretability trade-off.
• EBM's additive structure allows for clear, visualizable insights into individual feature and interaction contributions, providing transparent explanations for both global model behavior and specific predictions.

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