Area under the ROC curve of each class against the rest, using the a priori class distribution
roc_aunp() is a multiclass metric that computes the area under the ROC curve of each class against the rest, using the a priori class distribution. This is equivalent to roc_auc(estimator = "macro_weighted").
roc_aunp(data, ...) ## S3 method for class 'data.frame' roc_aunp(data, truth, ..., na_rm = TRUE, case_weights = NULL, options = list()) roc_aunp_vec( truth, estimate, na_rm = TRUE, case_weights = NULL, options = list(), ... )
data: A data.frame containing the columns specified by truth and ....
...: A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. There should be as many columns as factor levels of truth.
truth: The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.
na_rm: A logical value indicating whether NA
values should be stripped before the computation proceeds.
case_weights: The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().
options: [deprecated]
No longer supported as of yardstick 1.0.0. If you pass something here it will be ignored with a warning.
Previously, these were options passed on to pROC::roc(). If you need support for this, use the pROC package directly.
estimate: A matrix with as many columns as factor levels of truth. It is assumed that these are in the same order as the levels of truth.
A tibble with columns .metric, .estimator, and .estimate and 1 row of values.
For grouped data frames, the number of rows returned will be the same as the number of groups.
For roc_aunp_vec(), a single numeric value (or NA).
There is no common convention on which factor level should automatically be considered the "event" or "positive" result when computing binary classification metrics. In yardstick, the default is to use the first level. To alter this, change the argument event_level to "second" to consider the last level of the factor the level of interest. For multiclass extensions involving one-vs-all comparisons (such as macro averaging), this option is ignored and the "one" level is always the relevant result.
This multiclass method for computing the area under the ROC curve uses the a priori class distribution and is equivalent to roc_auc(estimator = "macro_weighted").
# Multiclass example # `obs` is a 4 level factor. The first level is `"VF"`, which is the # "event of interest" by default in yardstick. See the Relevant Level # section above. data(hpc_cv) # You can use the col1:colN tidyselect syntax library(dplyr) hpc_cv %>% filter(Resample == "Fold01") %>% roc_aunp(obs, VF:L) # Change the first level of `obs` from `"VF"` to `"M"` to alter the # event of interest. The class probability columns should be supplied # in the same order as the levels. hpc_cv %>% filter(Resample == "Fold01") %>% mutate(obs = relevel(obs, "M")) %>% roc_aunp(obs, M, VF:L) # Groups are respected hpc_cv %>% group_by(Resample) %>% roc_aunp(obs, VF:L) # Vector version # Supply a matrix of class probabilities fold1 <- hpc_cv %>% filter(Resample == "Fold01") roc_aunp_vec( truth = fold1$obs, matrix( c(fold1$VF, fold1$F, fold1$M, fold1$L), ncol = 4 ) )
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). "An experimental comparison of performance measures for classification". Pattern Recognition Letters. 30 (1), pp 27-38.
roc_aunu() for computing the area under the ROC curve of each class against the rest, using the uniform class distribution.
Other class probability metrics: average_precision(), brier_class(), classification_cost(), gain_capture(), mn_log_loss(), pr_auc(), roc_auc(), roc_aunu()
Julia Silge
Useful links