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About Towards Interrogating Discriminative Machine Learn
This document summarizes a research paper that proposes a new technique for interrogating discriminative machine learning models. The technique augments a Bayesian regression mixture model with multiple elastic nets to extract global explanations that can approximate the behavior of any target machine learning model. This global approximation approach not only explains individual predictions, but also provides insights into a model's overall strengths and weaknesses by discovering critical patterns in high-dimensional data. The authors evaluate their technique on tasks involving text and image data.
Detailed Information
| Author: | ['Gabriel Alalu'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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