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Leveraging Black-box Models to Assess Feature Importance

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RCLW01 - Uncertainty in multivariate, non-Euclidean, and functional spaces: theory and practice

Understanding the impact of changes in features on the unconditional distribution of outcomes is crucial for various applications. Despite their predictive accuracy, existing black-box models are limited in addressing such questions. In this work, we propose a novel approximation method to compute feature importance curves, which quantify changes across the quantiles of the outcome distribution due to shifts in features. Our approach leverages pre-trained black-box models, combining their predictive strength with interpretation. Through extensive simulations and real-world data applications, we show that our method delivers sparse, reliable results while maintaining computational efficiency, making it a practical tool for interpretation.

This talk is part of the Isaac Newton Institute Seminar Series series.

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