#!/usr/bin/env python # encoding: utf-8 ''' @author: shuhan Wei @software: pycharm @file: treeFore.py @time: 18-9-13 下午6:33 @desc:树回归预测 ''' import regTrees import numpy as np def regTreeEval(model, inDat): """ 函数说明:回归树的单个节点预测值 :param model: 某一节点...
Regression tree: rpart(formula = Salary ~ Level, data = dataset, control = rpart.control(minsplit = 1)) Variables actually used in tree construction: [1] Level Root node error: 8.0662e+11/10 = 8.0662e+10 n= 10 CP nsplit rel error xerror xstd 1 0.776386 0 1.000000 1.2346 0.78351 ...
重要~ 一.决策树(Decision Tree).口袋(Bagging),自适应增 ... CART决策树(分类回归树)分析及应用建模 一.CART决策树模型概述(Classification And Regression Trees) 决策树是使用类似于一棵树的结构来表示类的划分,树的构建可以看成是变量(属性)选择的过程,内部节 ... 决策树的剪枝,分类回归树CART 决策树的剪...
#建立树模型要权衡两方面问题,一个是要拟合得使分组后的变异较小,另一个是要防止过度拟合,而使模型的误差过大,前者的参数是CP,后者的参数是Xerror。所以要在Xerror最小的情况下,也使CP尽量小。如果认为树模型过于复杂,我们需要对其进行修剪 #首先观察模型的误差等数据 printcp(fit) Regression tree: rpart(form...
## 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. ...
如果认为树模型过于复杂,我们需要对其进行修剪 #首先观察模型的误差等数据 printcp(fit) Regression tree: rpart(formula = formula, data = bodyfat) Variables actually used in tree construction: [1] hipcirc waistcirc Root node error: 8536/71 = 120.23 n= 71 CP nsplit rel error xerror xstd 1 ...
分类回归树(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。分类回归树 既可以用于创建分类树 (classification tree),也可以用于创建回归树 (regression Tree) 回归树:用平方残差 (square of residual) 最小化准则来选择特征,叶子上是实数值 ...
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 = ...
## Step 3: Training a model on the data ---# regression tree using rpartlibrary(rpart) m.rpart <- rpart(quality ~ ., data = wine_train)# get basic information about the treem.rpart# get more detailed information about the treesummary(m.rpart)# use the rpart.plot package to cr...
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 0.0000 0.3230...