Customer Validation of Commercial Predictive Models

T. Bruckhaus, William E. Guthrie
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引用次数: 1

Abstract

A central need in the emerging business of model-based prediction is to enable customers to validate the accuracy of a predictive product. This paper discusses how analysts can evaluate data mining models and their inferences from the customer viewpoint, where the customer is not particularly knowledgeable in data mining. To date, academia has focused primarily on the validation of algorithms through mathematical metrics and benchmarking studies. This type of validation is not sufficient in the business context, where organizations must validate specific models in terms that customers can understand quickly and effortlessly. We describe our predictive business and our customer validation needs. To that end, we discuss examples of customer needs, review issues associated with model validation, and point out how academic research may help to address these business needs.
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商业预测模型的客户验证
在新兴的基于模型的预测业务中,一个核心需求是使客户能够验证预测产品的准确性。本文讨论了分析人员如何从客户的角度评估数据挖掘模型及其推断,而客户在数据挖掘方面并不是特别了解。迄今为止,学术界主要关注通过数学度量和基准研究来验证算法。这种类型的验证在业务上下文中是不够的,在业务上下文中,组织必须用客户能够快速且毫不费力地理解的术语来验证特定的模型。我们描述了我们的预测业务和我们的客户验证需求。为此,我们将讨论客户需求的示例,回顾与模型验证相关的问题,并指出学术研究如何有助于解决这些业务需求。
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