ID3和C4.5算法，生成的决策树是多叉树，只能处理分类不能处理回归。而CART（classification and regression tree）分类回归树算法，既可用于分类也可用于回归。 分类树的输出是样本的类别， 回归树的输出是一个实…

摘要：分类与回归树(Classification and Regression Tree, CART)是一种经典的决策树，可以用来处理涉及连续数据的分类或者回归任务，它和它的变种在当前的工业领域应用非常广泛。本文对这种决策树的思想、结构、训…

2024年7月17日 · CART (Classification and Regression Trees) 是一种基于决策树的分类回归算法。它可以用于二分类问题、多分类问题以及回归问题。CART算法通过对数据集进行递归划分，构建一棵二叉树，每个叶子节点对应一个分类或者回归结果。

2020年8月12日 · cart树是一种强大的决策树算法，适用于分类和回归任务。通过递归划分数据集、选择最优特征及其划分点，cart树能够生成非常有效的决策模型。然而，cart树也容易发生过拟合，因此需要使用剪枝技术来优化模型的复杂度。

2018年11月8日 · Xgboost以CART决策树为子模型，通过Gradient Tree Boosting实现多棵CART树的集成学习，得到最终模型。 XGBoost 的最终模型构建： 引用陈天奇的论文，我们的数据为：D={ ( xi,yi ) } ( ∣D∣=n,xi∈Rm,yi∈R ) \mathcal{D}=\left\{\left ( \mathbf{x}_{i},

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