`roc_auc()`

is a metric that computes the area under the ROC curve. See
`roc_curve()`

for the full curve.

## Usage

```
roc_auc(data, ...)
# S3 method for data.frame
roc_auc(
data,
truth,
...,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL,
options = list()
)
roc_auc_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL,
options = list(),
...
)
```

## Arguments

- 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. If`truth`

is binary, only 1 column should be selected. Otherwise, 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.- estimator
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"`

if`truth`

is binary,`"hand_till"`

if`truth`

has >2 levels and`case_weights`

isn't specified, or`"macro"`

if`truth`

has >2 levels and`case_weights`

is specified (in which case`"hand_till"`

isn't well-defined).- na_rm
A

`logical`

value indicating whether`NA`

values should be stripped before the computation proceeds.- event_level
A single string. Either

`"first"`

or`"second"`

to specify which level of`truth`

to consider as the "event". This argument is only applicable when`estimator = "binary"`

. The default uses an internal helper that generally defaults to`"first"`

, however, if the deprecated global option`yardstick.event_first`

is set, that will be used instead with a warning.- 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.- 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
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`

.

## Value

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`

).

## Details

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`

.

Note that you can't combine `estimator = "hand_till"`

with `case_weights`

.

## Relevant Level

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.

## Multiclass

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.
Additionally, while other multiclass techniques will return `NA`

if any
levels in `truth`

occur zero times in the actual data, the Hand-Till method
will simply ignore those levels in the averaging calculation, with a warning.

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).

## References

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()`

,
`classification_cost()`

,
`gain_capture()`

,
`mn_log_loss()`

,
`pr_auc()`

,
`roc_aunp()`

,
`roc_aunu()`

## Examples

```
# ---------------------------------------------------------------------------
# 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 × 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 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc hand_till 0.813
# 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 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc hand_till 0.813
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
roc_auc(obs, VF:L)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 roc_auc hand_till 0.813
#> 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 0.828
#> 10 Fold10 roc_auc hand_till 0.812
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
roc_auc(obs, VF:L, estimator = "macro_weighted")
#> # A tibble: 10 × 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] 0.8131924
```