Navigating Random Forests and related advances in algorithmic modeling
Navigating Random Forests and related advances in algorithmic modeling
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DOI：
10.1214/07-ss033
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发表时间：
2009-01-01
影响因子：
3.3
通讯作者：
Siroky, David S.
中科院分区：
文献类型：
--
作者：
Siroky, David S.
本文讨论了当前非参数随机森林的方法学研究。它提供了随机森林的简短知识历史，涵盖 CART、boosting 和 bagging 方法。然后介绍了研究人员可视化结果的主要方法、协变量和响应之间的关系以及袋外测试集误差。此外，本文还考虑了随机森林中普遍一致性和重要性测试的当前研究。最后，讨论了随机森林的几种用途，并确定了可用的软件。
This article addresses current methodological research on non-parametric Random Forests. It provides a brief intellectual history of Random Forests that covers CART, boosting and bagging methods. It then introduces the primary methods by which researchers can visualize results, the relationships between covariates and responses, and the out-of-bag test set error. In addition, the article considers current research on universal consistency and importance tests in Random Forests. Finally, several uses for Random Forests are discussed, and available software is identified.