322 citations · 359 across the 5 of their papers we have counts for
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cs.LG2019★ 322 cited
InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch +1
InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpret…
cs.LG2018
Axiomatic Interpretability for Multiclass Additive Models
Xuezhou Zhang, Sarah Tan, Paul Koch +3
Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining…