Document detail
ID

oai:arXiv.org:2404.13224

Topic
Computer Science - Machine Learnin...
Author
Sumiya, Yuta shouno, Hayaru
Category

Computer Science

Year

2024

listing date

4/24/2024

Keywords
method
Metrics

Abstract

Machine-learning models, which are known to accurately predict patterns from large datasets, are crucial in decision making.

Consequently, counterfactual explanations-methods explaining predictions by introducing input perturbations-have become prominent.

These perturbations often suggest ways to alter the predictions, leading to actionable recommendations.

However, the current techniques require resolving the optimization problems for each input change, rendering them computationally expensive.

In addition, traditional encoding methods inadequately address the perturbations of categorical variables in tabular data.

Thus, this study propose FastDCFlow, an efficient counterfactual explanation method using normalizing flows.

The proposed method captures complex data distributions, learns meaningful latent spaces that retain proximity, and improves predictions.

For categorical variables, we employed TargetEncoding, which respects ordinal relationships and includes perturbation costs.

The proposed method outperformed existing methods in multiple metrics, striking a balance between trade offs for counterfactual explanations.

The source code is available in the following repository: https://github.com/sumugit/FastDCFlow.

;Comment: 11 pages, 5 figures, 8 tables

Sumiya, Yuta,shouno, Hayaru, 2024, Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data

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