Data resampling
The first step in a machine learning project is to create training and test datasets for model fitting and evaluation. The test dataset provides an estimate of how your model will perform on new data and helps to guard against overfitting.
You will be working with the telecom_df
dataset which contains information on customers of a telecommunications company. The outcome variable is canceled_service
and it records whether a customer canceled their contract with the company. The predictor variables contain information about customers' cell phone and Internet usage as well as their contract type and monthly charges.
The telecom_df
tibble has been loaded into your session.
This exercise is part of the course
Modeling with tidymodels in R
Exercise instructions
- Create an
rsample
object,telecom_split
, that contains the instructions for randomly splitting thetelecom_df
data into training and test datasets.- Allocate 75% of the data into training and stratify the results by
canceled_service
.
- Allocate 75% of the data into training and stratify the results by
- Pass the
telecom_split
object to the appropriatersample
functions to create the training and test datasets. - Check the number of rows in each datasets by passing them to the
nrow()
function.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create data split object
telecom_split <- ___(___, prop = ___,
strata = ___)
# Create the training data
telecom_training <- ___ %>%
___
# Create the test data
telecom_test <- ___ %>%
___
# Check the number of rows
nrow(___)
nrow(___)