2020
DOI: 10.48550/arxiv.2006.16793
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Counterfactual explanation of machine learning survival models

Abstract: A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions. A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced. This condition is based on using a distance between mean times to event. It… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 66 publications
(102 reference statements)
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?