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 data.frame
roc_aunp(data, truth, ..., options = list(), na_rm = TRUE)

roc_aunp_vec(truth, estimate, options = list(), na_rm = TRUE, ...)



A data.frame containing the truth and estimate columns.


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.


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.


A list of named options to pass to pROC::roc() such as direction or smooth. These options should not include response, predictor, levels, or quiet.


A logical value indicating whether NA values should be stripped before the computation proceeds.


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).


Like the other ROC AUC metrics, roc_aunp() defaults to allowing pROC::roc() control the direction of the computation, but allows you to control this by passing options = list(direction = "<") or any other allowed direction value. pROC advises setting the direction when doing resampling so that the AUC values are not biased upwards.

Generally, an ROC AUC value is between 0.5 and 1, with 1 being a perfect prediction model. If your value is between 0 and 0.5, then this implies that you have meaningful information in your model, but it is being applied incorrectly because doing the opposite of what the model predicts would result in an AUC >0.5.

Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest by running: options(yardstick.event_first = FALSE). 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").


Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). "An experimental comparison of performance measures for classification". Pattern Recognition Letters. 30 (1), pp 27-38.

See also

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(), gain_capture(), mn_log_loss(), pr_auc(), roc_auc(), roc_aunu()


# 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)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_aunp macro 0.880
# 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)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_aunp macro 0.880
# Groups are respected hpc_cv %>% group_by(Resample) %>% roc_aunp(obs, VF:L)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 roc_aunp macro 0.880 #> 2 Fold02 roc_aunp macro 0.873 #> 3 Fold03 roc_aunp macro 0.906 #> 4 Fold04 roc_aunp macro 0.867 #> 5 Fold05 roc_aunp macro 0.866 #> 6 Fold06 roc_aunp macro 0.865 #> 7 Fold07 roc_aunp macro 0.868 #> 8 Fold08 roc_aunp macro 0.865 #> 9 Fold09 roc_aunp macro 0.841 #> 10 Fold10 roc_aunp macro 0.869
# 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 ) )
#> [1] 0.8795121
# --------------------------------------------------------------------------- # Options for `pROC::roc()` # Pass options via a named list and not through `...`! roc_aunp( hpc_cv, obs, VF:L, options = list(smooth = TRUE) )
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_aunp macro 0.868