Compute the Recall metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute Recall, or asks how to score with Recall.
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---
name: recall
description: Compute the Recall metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute Recall, or asks how to score with Recall.
metadata:
skill_kind: metric
source_lib: torchmetrics
import_path: torchmetrics.Recall
source: library_introspection
---
# recall
> Metric `Recall` from `torchmetrics` (torchmetrics.Recall)
## When to invoke this skill
The user has predictions + ground truth and asks to evaluate with Recall, or
mentions `torchmetrics.Recall` directly, or wants the standard torchmetrics implementation.
## Reference signature
```python
from torchmetrics import Recall
# Recall(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, average: Optional[Literal['micro', 'macro', 'weighted', 'none']] = 'micro', multidim_average: Optional[Literal['global', 'samplewise']] = 'global', top_k: Optional[int] = 1, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```
## Library docstring
```
Compute `Recall`_.
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and
false negatives respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this
case is encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may
therefore be affected in turn.
This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
:class:`~torchmetrics.classification.BinaryRecall`,
:class:`~torchmetrics.classification.MulticlassRecall` and :class:`~torchmetrics.classification.MultilabelRecall`
for the specific details of each argument influence and examples.
Legacy Example:
>>> from torch import tensor
>>> preds = tensor([2, 0, 2, 1])
>>> target = tensor([1, 1, 2, 0])
>>> recall = Recall(task="multiclass", average='macro', num_classes=3)
>>> recall(preds, target)
tensor(0.3333)
>>> recall = Recall(task="multiclass", average='micro', num_classes=3)
>>> recall(preds, target)
tensor(0.2500)
```
## Quick recipe
```python
import torchmetrics as _m
score = _m.Recall(y_true, y_pred)
```
## Don'ts
- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.
