small doc fix

src/torchmetrics/classification/average_precision.py

@@ -45,7 +45,8 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
  .. math::
  AP = \sum{n} (R_n - R_{n-1}) P_n
 
- where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`.
+ 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).
 
  Accepts the following input tensors:
 
@@ -114,7 +115,8 @@ class MulticlassAveragePrecision(MulticlassPrecisionRecallCurve):
  .. math::
  AP = \sum{n} (R_n - R_{n-1}) P_n
 
- where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`.
+ 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).
 
  Accepts the following input tensors:
 
@@ -219,7 +221,8 @@ class MultilabelAveragePrecision(MultilabelPrecisionRecallCurve):
  .. math::
  AP = \sum{n} (R_n - R_{n-1}) P_n
 
- where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`.
+ 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).
 
  Accepts the following input tensors:
 

src/torchmetrics/functional/classification/average_precision.py

@@ -96,7 +96,8 @@ def binary_average_precision(
  .. math::
  AP = \sum{n} (R_n - R_{n-1}) P_n
 
- where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`.
+ 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).
 
  Accepts the following input tensors:
 
@@ -196,7 +197,8 @@ def multiclass_average_precision(
  .. math::
  AP = \sum{n} (R_n - R_{n-1}) P_n
 
- where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`.
+ 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).
 
  Accepts the following input tensors:
 
@@ -326,7 +328,8 @@ def multilabel_average_precision(
  .. math::
  AP = \sum{n} (R_n - R_{n-1}) P_n
 
- where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`.
+ 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).
 
  Accepts the following input tensors: