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Generating Simple, Conservative and Unifying Explanations for Logistic Regression Models

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conference contribution
posted on 2025-05-05, 04:34 authored by Ingrid ZukermanIngrid Zukerman, Sameen Maruf, Xuelin Situ, Cecile Paris, Gholamreza Haffari

We generate and compare three types of explanations of Machine Learning predictions: simple, conservative and unifying. Simple explanations are concise, conservative explanations address the surprisingness of a prediction, and unifying explanations convey the extent to which an ML model’s predictions are applicable.

Funding

Explaining the outcomes of complex computational models

Australian Research Council

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