https://rolkra.github.io/lets-grow-trees/
https://cambiotraining.github.io/intro-machine-learning/decision-trees.html
https://cambiotraining.github.io/intro-machine-learning/decision-trees.html
library(dplyr) library(explore) library(palmerpenguins) library(visNetwork) library(caret) mydata %>% explain_tree(target = val, out = "model") %>% visTree() library(rpart) library(visNetwork) fit <- rpart(y ~ x1 + x2 + x3, data = mydata, control = rpart.control(maxdepth=15, cp = 0.01)) visTree(fit, height = "600px", width = "1024px") tree_plot <- visTree(fit, data = mydata, nodesPopSize = TRUE, minNodeSize = 10, maxNodeSize = 30, height = "800px", width = "2048px") visSave(tree_plot, file = "C:\\R\\Plot\\tree.html")
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1 #
library(rpart)
library("rpart.utils")
library("rpart.plot")
fit<-rpart(Reliability~.,data=car.test.frame)
rpart.subrules.table(fit)
plotcp(fit)
rpart.plot(fit, type=4)
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2 #
library(rpart) model <- rpart(factor(is_out)~., data=training, method="class") plot(model, uniform=TRUE) text(model, use.n=T) library(rattle) fancyRpartPlot(model)
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3 #
#install.packages("tree")
library(tree)
ir.tr <- tree(Species ~., iris)
ir.tr
ir.tr1 <- snip.tree(ir.tr, nodes = c(12, 7))
summary(ir.tr1)
par(pty = "s")
plot(iris[, 3],iris[, 4], type="n",
xlab="petal length", ylab="petal width")
text(iris[, 3], iris[, 4], c("s", "c", "v")[iris[, 5]])
partition.tree(ir.tr1, add = TRUE, cex = 1.5)
# 1D example
ir.tr <- tree(Petal.Width ~ Petal.Length, iris)
plot(iris[,3], iris[,4], type="n", xlab="Length", ylab="Width")
partition.tree(ir.tr, add = TRUE, cex = 1.5)
partition.tree()襯 覃 螳 蠏碁殊 覲 . 
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5 谿瑚襭 #
- http://www.di.fc.ul.pt/~jpn/r/tree/tree.html --> Classification & Regression Trees, 譬れ.
DTreesO.pdf (![[http]](/moniwiki/imgs/http.png)
覲)--> R 螳 .
鏤