Visualizing decision trees

Load packages library(tidyverse) #remotes::install_github("grantmcdermott/parttree") library(parttree) library(rpart) library(rpart.plot) library(parsnip) library(titanic) library(tidyverse) Train learner Build the tree using parsnip with rpart as the model engine: set.seed(123) titanic_train$Survived = as.factor(titanic_train$Survived) ti_tree = decision_tree() %>% set_engine("rpart") %>% set_mode("classification") %>% fit(Survived ~ Pclass + Age, data = titanic_train) Plot the model partitions titanic_train %>% ggplot(aes(x=Pclass, y=Age)) + …