tidy.roc | R Documentation |
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'roc' tidy(x, ...)
x |
An |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in
|
A tibble::tibble()
with columns:
cutoff |
The cutoff used for classification. Observations with predicted probabilities above this value were assigned class 1, and observations with predicted probabilities below this value were assigned class 0. |
fpr |
False positive rate. |
tpr |
The true positive rate at the given cutoff. |
tidy()
, AUC::roc()
# load libraries for models and data library(AUC) # load data data(churn) # fit model r <- roc(churn$predictions, churn$labels) # summarize with tidiers + visualization td <- tidy(r) td library(ggplot2) ggplot(td, aes(fpr, tpr)) + geom_line() # compare the ROC curves for two prediction algorithms library(dplyr) library(tidyr) rocs <- churn %>% pivot_longer(contains("predictions"), names_to = "algorithm", values_to = "value" ) %>% nest(data = -algorithm) %>% mutate(tidy_roc = purrr::map(data, ~ tidy(roc(.x$value, .x$labels)))) %>% unnest(tidy_roc) ggplot(rocs, aes(fpr, tpr, color = algorithm)) + geom_line()
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