# Statscores

> Compute the StatScores metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute StatScores, or asks how to score with StatScores.

- Skill: `qhjqhj00/statscores` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/statscores`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/statscores/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/statscores

---


# statscores

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import StatScores

# StatScores(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 the number of true positives, false positives, true negatives, false negatives and the support.

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.BinaryStatScores`, :class:`~torchmetrics.classification.MulticlassStatScores`
and :class:`~torchmetrics.classification.MultilabelStatScores` for the specific details of each argument influence
and examples.

Legacy Example:
    >>> from torch import tensor
    >>> preds  = tensor([1, 0, 2, 1])
    >>> target = tensor([1, 1, 2, 0])
    >>> stat_scores = StatScores(task="multiclass", num_classes=3, average='micro')
    >>> stat_scores(preds, target)
    tensor([2, 2, 6, 2, 4])
    >>> stat_scores = StatScores(task="multiclass", num_classes=3, average=None)
    >>> stat_scores(preds, target)
    tensor([[0, 1, 2, 1, 1],
            [1, 1, 1, 1, 2],
            [1, 0, 3, 0, 1]])
```

## Quick recipe

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