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.
Cet exercice fait partie du cours
Modeling with tidymodels in R
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create cross validation folds
set.seed(290)
loans_folds <- ___(___, v = ___,
strata = ___)
loans_folds