内容提示: OverviewClassification and regression treesWei-Yin LohClassificationandregressiontreesaremachine-learningmethodsforconstructingpredictionmodelsfromdata.Themodelsareobtainedbyrecursivelypartitioningthe data space and fitting a simple prediction model within each partition. As aresult, the partitioning can ...
Loh, W.Y. (2011) Classification and regression trees. WIREs Data Mining and Knowledge Discovery 1: pp. 14-23Loh W (2011) Classification and regression trees. Data Min Knowl Disc 1:14–23W.-Y. Loh "Classification and regression trees", Wiley Interdiscipl. Rev., Data Mining Knowl...
monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees ...
Classification and Regression Trees(简称CART),指的是可用于分类或回归预测建模问题的决策树算法。在本节中,我们将重点介绍如何使用CART解决分类问题,并以Banknote数据集为例进行演示。 CART模型的表示形式是一棵二叉树。每个节点表示单个输入变量(X)和该变量的分割点(假定变量是数字化的)。树的叶节点(也称作...
本文主要介绍了 CART 中的分类树的构建算法原理,及遇到了不同类型的数据时,该算法会如何处理,当然这并不是分类树的全部,因为决策树容易导致过拟合的原因,在建树之后,往往会伴随着”剪枝“的操作,这些内容以及回归树部分会放在后面再做介绍。 参考:StatQuest: Decision Trees 关注作者:...
Asymptotic consistency of median regression trees (Classification and Regression Trees. Wadsworth, Belmont, 1984). In this article, we derive and discuss some general regularity conditions that can ensure ... P Chaudhuri - 《Journal of Statistical Planning & Inference》 被引量: 13发表: 2000年 加载...
CART(Classification and Regression Trees)是一种常用的决策树算法,既可以用于分类问题,也可以用于回归问题。CART算法由Breiman等人于1984年提出,是一种基于递归二分划分的贪婪算法。以下是对CART算法的详细解释: 1. 决策树的构建过程: CART算
摘要: Classification and Regression Trees (CART) represents a data-driven, model-based, nonparametric estimation method that implements the define-your-own-model approach. In other words, CART is a method...关键词: Discriminant analysis DOI: 10.1007/978-3-319-03629-8_10 被引量: 22 ...
最后我们一起学习了从多个特征维度来产生回归树,它和单维度特征的区别在于,每产生一个节点前,都需要计算每个特征的 及其对应的阈值,最后取其中最小的 对应的特征和阈值作为该节点。 参考资料:Regression Trees, Clearly Explained