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.
Este ejercicio forma parte del curso
Modeling with tidymodels in R
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Detailed cross validation results
dt_rs_results <- ___ %>%
collect_metrics(___)
# Explore model performance for decision tree
dt_rs_results %>%
group_by(___) %>%
summarize(min = ___,
median = ___,
max = ___)