Clarify language about one-vs-rest for classification metrics (#2051)

* docs improve

* suggestions

(cherry picked from commit d4ab66c)

src/torchmetrics/classification/auroc.py

@@ -172,6 +172,11 @@ class MulticlassAUROC(MulticlassPrecisionRecallCurve):
     multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
     corresponds to random guessing.
 
+    For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
+    classes as the negative, which is refered to as the one-vs-rest approach. One-vs-one is currently not supported by
+    this metric. By default the reported metric is then the average over all classes, but this behavior can be changed
+    by setting the ``average`` argument.
+
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
     - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` containing probabilities or logits

src/torchmetrics/classification/average_precision.py

@@ -171,6 +171,11 @@ class MulticlassAveragePrecision(MulticlassPrecisionRecallCurve):
     where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
     equivalent to the area under the precision-recall curve (AUPRC).
 
+    For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
+    classes as the negative, which is refered to as the one-vs-rest approach. One-vs-one is currently not supported by
+    this metric. By default the reported metric is then the average over all classes, but this behavior can be changed
+    by setting the ``average`` argument.
+
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
     - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` containing probabilities or logits

src/torchmetrics/classification/precision_recall_curve.py

@@ -224,6 +224,10 @@ class MulticlassPrecisionRecallCurve(Metric):
     The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the
     tradeoff between the two values can been seen.
 
+    For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
+    classes as the negative, which is refered to as the one-vs-rest approach. One-vs-one is currently not supported by
+    this metric.
+
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
     - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing

src/torchmetrics/classification/recall_fixed_precision.py

@@ -180,6 +180,10 @@ class MulticlassRecallAtFixedPrecision(MulticlassPrecisionRecallCurve):
     This is done by first calculating the precision-recall curve for different thresholds and the find the recall for
     a given precision level.
 
+    For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
+    classes as the negative, which is refered to as the one-vs-rest approach. One-vs-one is currently not supported by
+    this metric.
+
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
     - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor

src/torchmetrics/classification/roc.py

@@ -174,6 +174,10 @@ class MulticlassROC(MulticlassPrecisionRecallCurve):
     The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at
     different thresholds, such that the tradeoff between the two values can be seen.
 
+    For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
+    classes as the negative, which is refered to as the one-vs-rest approach. One-vs-one is currently not supported by
+    this metric.
+
     As input to ``forward`` and ``update`` the metric accepts the following input:
 
     - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor

src/torchmetrics/classification/specificity_sensitivity.py

@@ -133,6 +133,10 @@ class MulticlassSpecificityAtSensitivity(MulticlassPrecisionRecallCurve):
     This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the
     find the specificity for a given sensitivity level.
 
+    For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
+    classes as the negative, which is refered to as the one-vs-rest approach. One-vs-one is currently not supported by
+    this metric.
+
     Accepts the following input tensors:
 
     - ``preds`` (float tensor): ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each