Multiple additive regression trees with application in epidemiology
Multiple additive regression trees with application in epidemiology
复制标题
DOI：
10.1002/sim.1501
复制
发表时间：
2003-05-15
影响因子：
2
通讯作者：
Meulman, JJ
中科院分区：
文献类型：
--
作者：
Friedman, JH;Meulman, JJ
根据从过去的观测数据中获得的知识预测未来的结果是各种科学研究领域的常见应用。在本文中，预测将集中在不同级别的宫颈癌前病变和肿瘤。用于预测的统计工具当然应该具有预测准确性，并且优选地满足次要要求，诸如速度、易用性和所得到的预测模型的可解释性。一个新的自动化过程的基础上的扩展（称为“助推”）的回归和分类树（CART）模型。由此产生的工具是一个快速的“现成的分类和回归程序，在准确性方面与更多定制的方法具有竞争力，同时使用相当自动化（很少调整），并且非常健壮，特别是当应用于不太干净的数据时。额外的工具，提出了解释和可视化的结果，这样的多重加性回归树（MART）模型。版权所有（C）2003约翰威利父子有限公司。
Predicting future outcomes based on knowledge obtained from past observational data is a common application in a wide variety of areas of scientific research. In the present paper, prediction will be focused on various grades of cervical preneoplasia and neoplasia. Statistical tools used for prediction should of course possess predictive accuracy, and preferably meet secondary requirements such as speed, ease of use, and interpretability of the resulting predictive model. A new automated procedure based on an extension (called 'boosting') of regression and classification tree (CART) models is described. The resulting tool is a fast 'off-the-shelf procedure for classification and regression that is competitive in accuracy with more customized approaches, while being fairly automatic to use (little tuning), and highly robust especially when applied to less than clean data. Additional tools are presented for interpreting and visualizing the results of such multiple additive regression tree (MART) models. Copyright (C) 2003 John Wiley Sons, Ltd.