Leveraging Black-box Models to Assess Feature Importance

Add to your list(s) Download to your calendar using vCal

• Jing Zhou (University of East Anglia)
• Thursday 08 May 2025, 14:00-14:30
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact nobody.

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.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.