Counterfactual explanation of machine learning survival models
- URL: http://arxiv.org/abs/2006.16793v1
- Date: Fri, 26 Jun 2020 19:46:47 GMT
- Title: Counterfactual explanation of machine learning survival models
- Authors: Maxim S. Kovalev and Lev V. Utkin
- Abstract summary: It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints.
For other black-box models, it is proposed to apply the well-known Particle Swarm Optimization algorithm.
- Score: 5.482532589225552
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 is shown that the
counterfactual explanation problem can be reduced to a standard convex
optimization problem with linear constraints when the explained black-box model
is the Cox model. For other black-box models, it is proposed to apply the
well-known Particle Swarm Optimization algorithm. A lot of numerical
experiments with real and synthetic data demonstrate the proposed method.
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