Plot decision tree in R (Caret)

问题:

I have trained a dataset with rf method. For example:

ctrl <- trainControl(
                     method = "LGOCV", 
                     repeats = 3, 
                     savePred=TRUE,
                     verboseIter = TRUE,
                     preProcOptions = list(thresh = 0.95)
                    )

preProcessInTrain<-c("center", "scale")
metric_used<-"Accuracy"
model <- train(
               Output ~ ., data = training,
               method = "rf",
               trControl = ctrl,
               metric=metric_used,
               tuneLength = 10,
               preProc = preProcessInTrain
              )

After thath, I want to plot the decission tree, but when I wirte plot(model), I get this: plot(model).

If I write plot(model$finalModel), I get this : plot(model$finalModel)

I would like to plot the decission tree...

How can I do that? Thanks :)

回答1:

The model you are using is random forest, which is not a single decision tree, but an ensemble of a large number of trees. Plotting the final model will plot the error rates on the training and test datasets as # of trees are increased, something like the following.

If you want a single decision tree instead, you may like to train a CART model like the following:

model <- train(
  Species ~ ., data = training,
  method = "rpart",
  trControl = ctrl,
  metric=metric_used,
  tuneLength = 10,
  preProc = preProcessInTrain
)
library(rpart.plot)
rpart.plot(model$finalModel)

Now plotting the final model as above will plot the decision tree for you.