Sensitivityatspecificity

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

# sensitivityatspecificity

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import SensitivityAtSpecificity

# SensitivityAtSpecificity(task: Literal['binary', 'multiclass', 'multilabel'], min_specificity: 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 sensitivity value given the minimum specificity thresholds provided.

This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the
find the sensitivity for a given specificity 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.BinarySensitivityAtSpecificity`,
:class:`~torchmetrics.classification.MulticlassSensitivityAtSpecificity` and
:class:`~torchmetrics.classification.MultilabelSensitivityAtSpecificity` for the specific details of each argument
influence and examples.
```

## Quick recipe

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