49 citations · 100 across the 10 of their papers we have counts for
4 papers · 1 filter
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression
Ian Covert, Su-In Lee
The Shapley value concept from cooperative game theory has become a popular technique for interpreting ML models, but efficiently estimating these values remains challenging, parti…
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee
Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We descri…
Feature Removal Is a Unifying Principle for Model Explanation Methods
Ian Covert, Scott Lundberg, Su-In Lee
Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We examin…
Understanding Global Feature Contributions With Additive Importance Measures
Ian Covert, Scott Lundberg, Su-In Lee
Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability. To assess the role of…