How to drawing a decision tree?

Machine Learning and Modeling
,
1

This is my script for a decision tree in R:

    library(caret)
    library(rpart.plot)
    library(plyr)
    library(dplyr)
    
    data("iris")
    
    names(iris) = tolower(names(iris))
    
    table(iris$species)
    suppressMessages(library(caret))
    
    index = createDataPartition(y=iris$species, p=0.7, list=FALSE)
    
    train = iris[index,]
    test = iris[-index,]
    
    trainctrl <- trainControl(method = "cv", number = 5, verboseIter = FALSE)

dt.model <- train(species~., data=train, method = "rpart", 
                  tuneLength = 10,
                  preProcess = c("center", "scale"),
                  trControl = trainctrl,
                  metric="Kappa")

dt.predict <-predict(dt.model, test)
confusionMatrix(dt.predict, test$species)

How can I make the tree drawing with nodes?

2 
rpart.plot(dt.model$finalModel)
3

But I have this error:

Error in plot.new() : figure margins too large
4

is there another way to know which characteristics the model considers more important?

5

You can use caret varImp function to get variables importance.
Anyway the following code runs smoothly on my PC

library(caret)
library(rpart.plot)
library(plyr)
library(dplyr)

data("iris")

names(iris) = tolower(names(iris))

table(iris$species)
# suppressMessages(library(caret))

index = createDataPartition(y=iris$species, p=0.7, list=FALSE)

train = iris[index,]
test = iris[-index,]

trainctrl <- trainControl(method = "cv", number = 5, verboseIter = FALSE)

dt.model <- train(species~., data=train, method = "rpart", 
                  tuneLength = 11,
                  preProcess = c("center", "scale"),
                  trControl = trainctrl,
                  metric="Kappa")

dt.predict <-predict(dt.model, test)
dt.model
confusionMatrix(dt.predict, test$species)

rpart.plot(dt.model$finalModel)

varImp(dt.model)
6

Ok, now works thanks!

What is the meaning of Overall in this table?

            
> varImp(dt.model)
rpart variable importance
              Overall
petal.length  100.00
petal.width   100.00
sepal.length   37.59
sepal.width     0.00
7

It is not class specific, for details have a look at https://forum.posit.co/t/varimp-for-rpart-when-using/18584
HTH