The same definition as Keras
is used by default. This is equivalent to the `'micro'`

method in SciKit Learn
too. See docs.

## Usage

```
luz_metric_multiclass_auroc(
num_thresholds = 200,
thresholds = NULL,
from_logits = FALSE,
average = c("micro", "macro", "weighted", "none")
)
```

## Arguments

- num_thresholds
Number of thresholds used to compute confusion matrices. In that case, thresholds are created by getting

`num_thresholds`

values linearly spaced in the unit interval.- thresholds
(optional) If threshold are passed, then those are used to compute the confusion matrices and

`num_thresholds`

is ignored.- from_logits
If

`TRUE`

then we call`torch::nnf_softmax()`

in the predictions before computing the metric.- average
The averaging method:

`'micro'`

: Stack all classes and computes the AUROC as if it was a binary classification problem.`'macro'`

: Finds the AUCROC for each class and computes their mean.`'weighted'`

: Finds the AUROC for each class and computes their weighted mean pondering by the number of instances for each class.`'none'`

: Returns the AUROC for each class in a list.

## Details

**Note** that class imbalance can affect this metric unlike
the AUC for binary classification.

Currently the AUC is approximated using the 'interpolation' method described in Keras.

## See also

Other luz_metrics:
`luz_metric_accuracy()`

,
`luz_metric_binary_accuracy_with_logits()`

,
`luz_metric_binary_accuracy()`

,
`luz_metric_binary_auroc()`

,
`luz_metric_mae()`

,
`luz_metric_mse()`

,
`luz_metric_rmse()`

,
`luz_metric()`

## Examples

```
if (torch::torch_is_installed()) {
library(torch)
actual <- c(1, 1, 1, 0, 0, 0) + 1L
predicted <- c(0.9, 0.8, 0.4, 0.5, 0.3, 0.2)
predicted <- cbind(1-predicted, predicted)
y_true <- torch_tensor(as.integer(actual))
y_pred <- torch_tensor(predicted)
m <- luz_metric_multiclass_auroc(thresholds = as.numeric(predicted),
average = "micro")
m <- m$new()
m$update(y_pred[1:2,], y_true[1:2])
m$update(y_pred[3:4,], y_true[3:4])
m$update(y_pred[5:6,], y_true[5:6])
m$compute()
}
#> [1] 0.9027778
```