Recallatfixedprecision

---
name: recallatfixedprecision
description: Compute the RecallAtFixedPrecision metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute RecallAtFixedPrecision, or asks how to score with RecallAtFixedPrecision.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.RecallAtFixedPrecision
  source: library_introspection
---

# recallatfixedprecision

> Metric `RecallAtFixedPrecision` from `torchmetrics` (torchmetrics.RecallAtFixedPrecision)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with RecallAtFixedPrecision, or
mentions `torchmetrics.RecallAtFixedPrecision` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics import RecallAtFixedPrecision

# RecallAtFixedPrecision(task: Literal['binary', 'multiclass', 'multilabel'], min_precision: float, thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, num_classes: Optional[int] = None, num_labels: Optional[int] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```

## Library docstring

```
Compute the highest possible recall value given the minimum precision thresholds provided.

This is done by first calculating the precision-recall curve for different thresholds and the find the recall for
a given precision level.

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.BinaryRecallAtFixedPrecision`,
:class:`~torchmetrics.classification.MulticlassRecallAtFixedPrecision` and
:class:`~torchmetrics.classification.MultilabelRecallAtFixedPrecision` for the specific details of each argument
influence and examples.
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.RecallAtFixedPrecision(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)`.