Rootmeansquarederrorusingslidingwindow

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

# rootmeansquarederrorusingslidingwindow

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import RootMeanSquaredErrorUsingSlidingWindow

# _RootMeanSquaredErrorUsingSlidingWindow(window_size: int = 8, **kwargs: dict[str, typing.Any]) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> from torch import rand
>>> preds = rand(4, 3, 16, 16)
>>> target = rand(4, 3, 16, 16)
>>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow()
>>> rmse_sw(preds, target)
tensor(0.4158)
```

## Quick recipe

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