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SYSTEMS AND METHODS FOR DETECTING NON-CAUSAL DEPENDENCIES IN MACHINE LEARNING MODELS

机译:用于检测机器学习模型中的非因果依赖性的系统和方法

摘要

A non-causal dependency in a machine learning model can bias the performance of the machine learning model. Systems and methods for detecting non-causal dependencies in machine learning models are provided. According to an embodiment, a method includes generating a plurality of data samples from a particular data sample, the plurality of data samples including a modified data sample that differs from the particular data sample by non-causal data, the non-causal data having a non-causal relationship to the output of a machine learning model. The method also includes generating a plurality of results by inputting the plurality of data samples into the machine learning model. The method further includes determining, based on a comparison of the plurality of results, if the machine learning model is dependent on the non-causal data.
机译:机器学习模型中的非因果依赖性可以偏向机器学习模型的性能。提供了用于检测机器学习模型中的非因果依赖性的系统和方法。根据一个实施例,一种方法包括从特定数据样本生成多个数据采样,多个数据样本包括由非因果数据不同于特定数据样本的修改数据样本,具有具有a的非因果数据与机器学习模型输出的非因果关系。该方法还包括通过将多个数据样本输入到机器学习模型中来生成多个结果。该方法还包括基于多个结果的比较来确定,如果机器学习模型取决于非因果数据。

著录项

  • 公开/公告号US2021182730A1

    专利类型

  • 公开/公告日2021-06-17

    原文格式PDF

  • 申请/专利权人 SHOPIFY INC.;

    申请/专利号US201916711538

  • 发明设计人 GREGORY CLARKE;

    申请日2019-12-12

  • 分类号G06N20;

  • 国家 US

  • 入库时间 2022-08-24 19:23:35

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