Comparing model performance profiles
The benefit of the collect_metrics()
function is that it returns a tibble of cross validation results. This makes it easy to calculate custom summary statistics with the dplyr
package.
In this exercise, you will use dplyr
to explore the cross validation results of your decision tree and logistic regression models.
Your cross validation results, loans_dt_rs
and loans_logistic_rs
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.
# Detailed cross validation results
dt_rs_results <- ___ %>%
collect_metrics(___)
# Explore model performance for decision tree
dt_rs_results %>%
group_by(___) %>%
summarize(min = ___,
median = ___,
max = ___)