论文标题
VCNET：现实反事实的自我解释模型
VCNet: A self-explaining model for realistic counterfactual generation
论文作者
论文摘要
反事实解释是对机器学习决策做出本地解释的常见方法。对于给定的实例，这些方法旨在找到特征值的最小修改，以改变机器学习模型做出的预测决策。反事实解释的挑战之一是有效地产生了现实的反事实。为了应对这一挑战，我们提出了VCNET变量计数器Net-A模型架构，该模型结构结合了一个共同训练的预测指标和反事实发电机，以进行回归或分类任务。 VCNET能够产生预测，并生成反事实解释，而不必解决另一个最小化问题。我们的贡献是与预测类别的分布的产生。这是通过以连接训练方式进行有条件地到达预测变量输出的各种自动编码器来完成的。我们在表格数据集以及几个可解释性指标上介绍了经验评估。结果与最新方法具有竞争力。
Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual explanation is the efficient generation of realistic counterfactuals. To address this challenge, we propose VCNet-Variational Counter Net-a model architecture that combines a predictor and a counterfactual generator that are jointly trained, for regression or classification tasks. VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem. Our contribution is the generation of counterfactuals that are close to the distribution of the predicted class. This is done by learning a variational autoencoder conditionally to the output of the predictor in a join-training fashion. We present an empirical evaluation on tabular datasets and across several interpretability metrics. The results are competitive with the state-of-the-art method.
