Bagging 在上一篇文章《用机器学习做预测之三:广义线性模型与核回归》中,我们说到广义线性模型考虑了被预测变量与预测变量之间的非线性关系,但没有考虑到预测变量之间的交互关系,在本篇文章中,我们将介绍将预测变量之间的复杂关系纳入考量的回归树方法及其衍生模型。回归树(regression tree)模型基于二叉树(binary tree)...
idx = sample(1:n, size=n, replace=TRUE) L_tree[[s]] = rpart(as.(PR)~.) 而对于汇总部分,只需取预测概率的平均值即可 html p = function(x){ unlist(lapply(1:1000,function(z) predict(L_tree[z],newdata,)[,2]) 因为在这个例子中,我们无法实现预测的可视化,让我们在较小的数据集上运行...
idx = sample(1:n, size=n, replace=TRUE) L_tree[[s]]= rpart(as.(PR)~.) 而对于汇总部分,只需取预测概率的平均值即可 html p =function(x){unlist(lapply(1:1000,function(z)predict(L_tree[z],newdata,)[,2]) 因为在这个例子中,我们无法实现预测的可视化,让我们在较小的数据集上运行同样的...
library(tree) library(ISLR) attach(Hitters) # 删除NA数据 Hitters<- na.omit(Hitters) # log转换Salary使其更正态分布 hist(Hitters$Salary) Hitters$Salary <- log(Hitters$Salary) hist(Hitters$Salary) summary(tree.fit) ## ## Regression tree: ## tree(formula = Salary ~ Hits + Years, data =...
GBDT(Gradient Boosting Decision Tree) 又叫 MART(Multiple Additive Regression Tree),是一种迭代的决策树算法,该算法由多棵决策树组成,所有树的结论累加起来做最终答案。它在被提出之初就和SVM一起被认为是泛化能力较强的算法。 GBDT中的树是回归树(不是分类树),GBDT用来做回归预测,调整后也可以用于分类。
Tree-structured classification and regression are nonparametric computationally intensive methods that have greatly increased in popularity during the past several years. They can be applied to data sets having both a large number of cases and a large number of variables, and they are extremely ...
Regression Tree),是一种用于回归的机器学习算法,该算法由多棵决策树组成(但GBDT是回归树,不是分类决策树),所有树的结论累加起来做最终答案。当把目标函数做变换后,该算法亦可用于分类或排序 每日一练之梯度提升树GBDT 一轮轮的迭代下去。GBDT也是迭代,使用了前向分布算法,但是弱学习器限定了只能使用CART回归树...
summary(tree.fit)## ## Regression tree:## tree(formula = Salary ~ Hits + Years, data = Hitters)## Number of terminal nodes: 8 ## Residual mean deviance: 0.271 = 69.1 / 255 ## Distribution of residuals:## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -2.2400 -0.2980 -0.0365 ...
summary(tree.fit) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ## ## Regression tree:##tree(formula=Salary~Hits+Years,data=Hitters)## Numberofterminal nodes:8## Residual mean deviance:0.271=69.1/255## Distributionofresiduals:## Min.1st Qu.Median Mean 3rd Qu.Max.##-2.2400-0.2980-0.0...
summary(tree.fit) 1. AI检测代码解析 ## ## Regression tree: ## tree(formula = Salary ~ Hits + Years, data = Hitters) ## Number of terminal nodes: 8 ## Residual mean deviance: 0.271 = 69.1 / 255 ## Distribution of residuals: ...