Understand
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.
- Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks.
- Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent.
- We provide three types of explanations that are simpler than, but consistent with, the global model.
Built on
A threshold of ln n for approximating set cover (preliminary version)
Uriel Feige · 1996
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A threshold of ln n for approximating set cover
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