Measuring performance with cross validation
Cross validation is a method that uses training data to provide multiple estimates of model performance. When trying different model types on your data, it is important to study their performance profile to help decide which model type performs consistently well.
In this exercise, you will perform cross validation with your decision tree model workflow to explore its performance.
The training data, loans_training, and your workflow object, loans_dt_wkfl, have been loaded into your session.
This exercise is part of the course
Modeling with tidymodels in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create cross validation folds
set.seed(290)
loans_folds <- ___(___, v = ___,
strata = ___)
loans_folds