Build Decision Tree Classification - r

Question

I have two datasets , partb_data1 and partb_data2 . Given sample of customers of a bank that reflects the characteristics of the clients and whether the bank continues to work with them or not (Churn). Exited: Churn (1 if he has left the bank and 0 if he continues to work with it). Im using partb_data1 as train set and partb_data2 as test set.
Here is my data :
> dput(head(partb_data1))
structure(list(RowNumber = 1:6, CustomerId = c(15634602L, 15647311L,
15619304L, 15701354L, 15737888L, 15574012L), Surname = c("Hargrave",
"Hill", "Onio", "Boni", "Mitchell", "Chu"), CreditScore = c(619L,
608L, 502L, 699L, 850L, 645L), Geography = c("France", "Spain",
"France", "France", "Spain", "Spain"), Gender = c("Female", "Female",
"Female", "Female", "Female", "Male"), Age = c(42L, 41L, 42L,
39L, 43L, 44L), Tenure = c(2L, 1L, 8L, 1L, 2L, 8L), Balance = c(0,
83807.86, 159660.8, 0, 125510.82, 113755.78), NumOfProducts = c(1L,
1L, 3L, 2L, 1L, 2L), HasCrCard = c(1L, 0L, 1L, 0L, 1L, 1L), IsActiveMember = c(1L,
1L, 0L, 0L, 1L, 0L), EstimatedSalary = c(101348.88, 112542.58,
113931.57, 93826.63, 79084.1, 149756.71), Exited = c(1L, 0L,
1L, 0L, 0L, 1L)), row.names = c(NA, 6L), class = "data.frame")
> dput(head(partb_data2))
structure(list(RowNumber = 8001:8006, CustomerId = c(15629002L,
15798053L, 15753895L, 15595426L, 15645815L, 15632848L), Surname = c("Hamilton",
"Nnachetam", "Blue", "Madukwe", "Mills", "Ferrari"), CreditScore = c(747L,
707L, 590L, 603L, 615L, 634L), Geography = c("Germany", "Spain",
"Spain", "Spain", "France", "France"), Gender = c("Male", "Male",
"Male", "Male", "Male", "Female"), Age = c(36L, 32L, 37L, 57L,
45L, 36L), Tenure = c(8L, 9L, 1L, 6L, 5L, 1L), Balance = c(102603.3,
0, 0, 105000.85, 0, 69518.95), NumOfProducts = c(2L, 2L, 2L,
2L, 2L, 1L), HasCrCard = c(1L, 1L, 0L, 1L, 1L, 1L), IsActiveMember = c(1L,
0L, 0L, 1L, 1L, 0L), EstimatedSalary = c(180693.61, 126475.79,
133535.99, 87412.24, 164886.64, 116238.39), Exited = c(0L, 0L,
0L, 1L, 0L, 0L)), row.names = c(NA, 6L), class = "data.frame")
I have created Classification trees in order to predict churn . Here follows the code:
library(tidyverse)
library(caret)
library(rpart)
library(rpart.plot)
# Split the data into training and test set
train.data <- head(partb_data1, 500)
test.data <- tail(partb_data2, 150)
# Build the model
modelb <- rpart(Exited ~., data = train.data, method = "class")
# Visualize the decision tree with rpart.plot
rpart.plot(modelb)
# Make predictions on the test data
predicted.classes <- modelb %>%
predict(test.data, type = "class")
head(predicted.classes)
# Compute model accuracy rate on test data
mean(predicted.classes == test.data$Exited)
### Pruning the tree :
# Fit the model on the training set
modelb2 <- train(
Exited ~., data = train.data, method = "rpart",
trControl = trainControl("cv", number = 10),
tuneLength = 10
)
# Plot model accuracy vs different values of
# cp (complexity parameter)
plot(modelb2)
# Print the best tuning parameter cp that
# maximizes the model accuracy
modelb2$bestTune
# Plot the final tree model
plot(modelb2$finalModel)
# Make predictions on the test data
predicted.classes <- modelb2 %>% predict(test.data)
# Compute model accuracy rate on test data
mean(predicted.classes == test.data$Exited)
Note: I have made test set from the partb_data2.
Is the procedure i follow right? I must make any changes in order to accomplish my target which is classification trees ? Your help would be trully welcome !
EDITED !!!

Answer 1

Your head(partb_data1$Exited, 500) isn't a data.frame. Because of the $ you take a subset of your partb_data1 data. It's only an integer-vector, so that can't work.
class(head(partb_data1$Exited, 500))
[1] "integer"

Answer 2

There are always a lot of procedure options.
But you're right with sepreate your Data into a trainings, and a testdataset. Its also possible to use a crossvalidation instead. You're using a crossvalidation on your trainingsset, thats normally not necessary, but also possible.
I think using your complete Data for the cv should also work, but what you're doing isn't wrong.