library("rplatypus")
evaluate(fit) # one row per model, on the validation split
evaluate(fit, split = "test")
# A split with no masks is refused by name rather than scored against nothing;
# `predict()` still works on it.Compare the trained models
Description
One row per model: what it is, how big it is, how long it trained, and how it scored.
Usage
evaluate(object, ...)
## S3 method for class 'platypus_fit'
evaluate(object, split = "validation", ...)
Arguments
object
|
A platypus_fit().
|
…
|
Unused. |
split
|
Which data to score on: “validation”, “train” or “test”.
|
Details
The loss column is only comparable between models trained on the same loss, which is why loss_function is there beside it. A Focal-Tversky of 0.05 is not better than a CCE-Dice of 0.14; it is not even the same question. Metrics stay comparable, because they measure the mask rather than the objective.
Value
A data frame, one row per model.
A note on the name
evaluate is also exported by the evaluate package, which knitr depends on, so the two mask each other depending on which was attached last. It is a generic here, so evaluate(fit) dispatches on the fit’s class whichever one you reach - and rplatypus::evaluate(fit) says so outright. The name is kept because it is the word for what this does; platypus_fit() carries a prefix only because fit is a generic in the tidymodels packages and a plain fit would have been theirs to own.