Models for Understanding Versus Models for Prediction
Models for Understanding Versus Models for Prediction
复制标题
用于理解的模型与用于预测的模型
DOI：
--
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
G. Saporta
中科院分区：
文献类型：
--
作者：
G. Saporta
根据一个标准的观点，统计建模包括建立一个随机现象的简约表示，通常基于应用领域专家的知识：模型的目的是提供对数据和产生数据的潜在机制的更好理解。另一方面，数据挖掘和KDD处理预测建模：模型仅仅是算法，模型的质量通过其预测新观测的性能来评估。在这篇文章中，我们对建模的这两个方面提出了一些一般性的考虑。
According to a standard point of view, statistical modelling consists in establishing a parsimonious representation of a random phenomenon, generally based upon the knowledge of an expert of the application field: the aim of a model is to provide a better understanding of data and of the underlying mechanism which have produced it. On the other hand, Data Mining and KDD deal with predictive modelling: models are merely algorithms and the quality of a model is assessed by its performance for predicting new observations. In this communication, we develop some general considerations about both aspects of modelling.