{"title":"预测模型标记语言的一致性标准","authors":"Rick Pechter","doi":"10.1145/1289612.1289613","DOIUrl":null,"url":null,"abstract":"One of the main objectives for the Predictive Model Markup Language (PMML) is to facilitate the exchange of models from one environment to another. For example, a model developed with one tool can be transferred via PMML to another tool for scoring. Or, a model can be documented in PMML and given to others for review, inspection or archival purposes. Exchanging predictive models between different products or environments requires a common understanding of the PMML specification. This understanding can be less than perfect, especially since PMML contains over 700 language elements, along with the ability to add product specific extensions. The result is that, even though there is a detailed PMML specification, models defined in PMML can vary in subtle ways from vendor to vendor. As pointed out in last year's KDD Workshop (DM-SSP 05), this lack of conformity reduces the usefulness of PMML and hampers the growth of its use by the data mining community [1]. A clear and compelling need for a conformance standard has been identified to improve the interoperability of PMML models, and to increase the reliability of PMML as a seamless, multi-vendor model exchange medium. This paper defines the state of the art in PMML and an approach under consideration for cross-vendor PMML conformance.","PeriodicalId":413380,"journal":{"name":"Proceedings of the 4th international workshop on Data mining standards, services and platforms","volume":"134 4‐6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Conformance standard for the predictive model markup language\",\"authors\":\"Rick Pechter\",\"doi\":\"10.1145/1289612.1289613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the main objectives for the Predictive Model Markup Language (PMML) is to facilitate the exchange of models from one environment to another. For example, a model developed with one tool can be transferred via PMML to another tool for scoring. Or, a model can be documented in PMML and given to others for review, inspection or archival purposes. Exchanging predictive models between different products or environments requires a common understanding of the PMML specification. This understanding can be less than perfect, especially since PMML contains over 700 language elements, along with the ability to add product specific extensions. The result is that, even though there is a detailed PMML specification, models defined in PMML can vary in subtle ways from vendor to vendor. As pointed out in last year's KDD Workshop (DM-SSP 05), this lack of conformity reduces the usefulness of PMML and hampers the growth of its use by the data mining community [1]. A clear and compelling need for a conformance standard has been identified to improve the interoperability of PMML models, and to increase the reliability of PMML as a seamless, multi-vendor model exchange medium. This paper defines the state of the art in PMML and an approach under consideration for cross-vendor PMML conformance.\",\"PeriodicalId\":413380,\"journal\":{\"name\":\"Proceedings of the 4th international workshop on Data mining standards, services and platforms\",\"volume\":\"134 4‐6\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th international workshop on Data mining standards, services and platforms\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1289612.1289613\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th international workshop on Data mining standards, services and platforms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1289612.1289613","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
摘要
预测模型标记语言(Predictive Model Markup Language, PMML)的主要目标之一是促进模型从一个环境到另一个环境的交换。例如,使用一种工具开发的模型可以通过PMML转移到另一种工具进行评分。或者,可以用PMML记录模型,并将其提供给其他人以供审查、检查或存档。在不同的产品或环境之间交换预测模型需要对PMML规范有共同的理解。这种理解可能不够完美,特别是因为PMML包含超过700种语言元素,以及添加特定于产品的扩展的能力。结果是,即使有详细的PMML规范,在PMML中定义的模型也会因供应商的不同而有细微的差异。正如在去年的KDD研讨会(DM-SSP 05)中指出的那样,这种一致性的缺乏降低了PMML的有用性,并阻碍了数据挖掘社区[1]对其使用的增长。已经确定了对一致性标准的明确和迫切的需求,以改进PMML模型的互操作性,并增加PMML作为无缝、多供应商模型交换媒介的可靠性。本文定义了PMML技术的现状,并考虑了跨厂商PMML一致性的方法。
Conformance standard for the predictive model markup language
One of the main objectives for the Predictive Model Markup Language (PMML) is to facilitate the exchange of models from one environment to another. For example, a model developed with one tool can be transferred via PMML to another tool for scoring. Or, a model can be documented in PMML and given to others for review, inspection or archival purposes. Exchanging predictive models between different products or environments requires a common understanding of the PMML specification. This understanding can be less than perfect, especially since PMML contains over 700 language elements, along with the ability to add product specific extensions. The result is that, even though there is a detailed PMML specification, models defined in PMML can vary in subtle ways from vendor to vendor. As pointed out in last year's KDD Workshop (DM-SSP 05), this lack of conformity reduces the usefulness of PMML and hampers the growth of its use by the data mining community [1]. A clear and compelling need for a conformance standard has been identified to improve the interoperability of PMML models, and to increase the reliability of PMML as a seamless, multi-vendor model exchange medium. This paper defines the state of the art in PMML and an approach under consideration for cross-vendor PMML conformance.