roc_auc() is a metric that computes the area under the ROC curve. See roc_curve() for the full curve.

roc_auc(data, ...)

# S3 method for data.frame
roc_auc(data, truth, ..., options = list(), estimator = NULL, na_rm = TRUE)

  options = list(),
  estimator = NULL,
  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. If truth is binary, only 1 column should be selected. Otherwise, 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.


One of "binary", "hand_till", "macro", or "macro_weighted" to specify the type of averaging to be done. "binary" is only relevant for the two class case. The others are general methods for calculating multiclass metrics. The default will automatically choose "binary" or "hand_till" based on truth.


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


If truth is binary, a numeric vector of class probabilities corresponding to the "relevant" class. Otherwise, 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_auc_vec(), a single numeric value (or NA).


For most methods, roc_auc() 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. However, the Hand, Till (2001) method assumes that the individual AUCs are all above 0.5, so if an AUC value below 0.5 is computed, then 1 is subtracted from it to get the correct result. When not using the Hand, Till method, 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.


The default multiclass method for computing roc_auc() is to use the method from Hand, Till, (2001). Unlike macro-averaging, this method is insensitive to class distributions like the binary ROC AUC case.

Macro and macro-weighted averaging are still provided, even though they are not the default. In fact, macro-weighted averaging corresponds to the same definition of multiclass AUC given by Provost and Domingos (2001).


Hand, Till (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems". Machine Learning. Vol 45, Iss 2, pp 171-186.

Fawcett (2005). "An introduction to ROC analysis". Pattern Recognition Letters. 27 (2006), pp 861-874.

Provost, F., Domingos, P., 2001. "Well-trained PETs: Improving probability estimation trees", CeDER Working Paper #IS-00-04, Stern School of Business, New York University, NY, NY 10012.

See also

roc_curve() for computing the full ROC curve.

Other class probability metrics: average_precision(), gain_capture(), mn_log_loss(), pr_auc(), roc_aunp(), roc_aunu()


# --------------------------------------------------------------------------- # Two class example # `truth` is a 2 level factor. The first level is `"Class1"`, which is the # "event of interest" by default in yardstick. See the Relevant Level # section above. data(two_class_example) # Binary metrics using class probabilities take a factor `truth` column, # and a single class probability column containing the probabilities of # the event of interest. Here, since `"Class1"` is the first level of # `"truth"`, it is the event of interest and we pass in probabilities for it. roc_auc(two_class_example, truth, Class1)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_auc binary 0.939
# --------------------------------------------------------------------------- # 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_auc(obs, VF:L)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_auc hand_till 0.831
# 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_auc(obs, M, VF:L)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_auc hand_till 0.831
# Groups are respected hpc_cv %>% group_by(Resample) %>% roc_auc(obs, VF:L)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 roc_auc hand_till 0.831 #> 2 Fold02 roc_auc hand_till 0.817 #> 3 Fold03 roc_auc hand_till 0.869 #> 4 Fold04 roc_auc hand_till 0.849 #> 5 Fold05 roc_auc hand_till 0.811 #> 6 Fold06 roc_auc hand_till 0.836 #> 7 Fold07 roc_auc hand_till 0.825 #> 8 Fold08 roc_auc hand_till 0.846 #> 9 Fold09 roc_auc hand_till 0.836 #> 10 Fold10 roc_auc hand_till 0.820
# Weighted macro averaging hpc_cv %>% group_by(Resample) %>% roc_auc(obs, VF:L, estimator = "macro_weighted")
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 roc_auc macro_weighted 0.880 #> 2 Fold02 roc_auc macro_weighted 0.873 #> 3 Fold03 roc_auc macro_weighted 0.906 #> 4 Fold04 roc_auc macro_weighted 0.867 #> 5 Fold05 roc_auc macro_weighted 0.866 #> 6 Fold06 roc_auc macro_weighted 0.865 #> 7 Fold07 roc_auc macro_weighted 0.868 #> 8 Fold08 roc_auc macro_weighted 0.865 #> 9 Fold09 roc_auc macro_weighted 0.841 #> 10 Fold10 roc_auc macro_weighted 0.869
# Vector version # Supply a matrix of class probabilities fold1 <- hpc_cv %>% filter(Resample == "Fold01") roc_auc_vec( truth = fold1$obs, matrix( c(fold1$VF, fold1$F, fold1$M, fold1$L), ncol = 4 ) )
#> [1] 0.8305172
# --------------------------------------------------------------------------- # Options for `pROC::roc()` # Pass options via a named list and not through `...`! roc_auc( two_class_example, truth = truth, Class1, options = list(smooth = TRUE) )
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 roc_auc binary 0.942