# Continuousrankedprobabilityscore

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

- Skill: `qhjqhj00/continuousrankedprobabilityscore` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/continuousrankedprobabilityscore`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/continuousrankedprobabilityscore/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/continuousrankedprobabilityscore

---


# continuousrankedprobabilityscore

> Metric `ContinuousRankedProbabilityScore` from `torchmetrics` (torchmetrics.regression.ContinuousRankedProbabilityScore)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.regression import ContinuousRankedProbabilityScore

# ContinuousRankedProbabilityScore(**kwargs: Any) -> None
```

## Library docstring

```
Computes continuous ranked probability score.

.. math::
    CRPS(F, y) = \int_{-\infty}^{\infty} (F(x) - 1_{x \geq y})^2 dx

where :math:`F` is the predicted cumulative distribution function and :math:`y` is the true target. The metric is
usually used to evaluate probabilistic regression models, such as forecasting models. A lower CRPS indicates a
better forecast, meaning that forecasted probabilities are closer to the true observed values. CRPS can also be
seen as a generalization of the brier score for non binary classification problems.

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~torch.Tensor`): Predicted float tensor with shape ``(N,d)``
- ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)``

As output of ``forward`` and ``compute`` the metric returns the following output:

- ``cosine_similarity`` (:class:`~torch.Tensor`): A float tensor with the cosine similarity

Args:
    reduction: how to reduce over the batch dimension using 'sum', 'mean' or 'none' (taking the individual scores)
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example:
    >>> from torch import randn
    >>> from torchmetrics.regression import ContinuousRankedProbabilityScore
    >>> preds = randn(10, 5)
    >>> target = randn(10)
    >>> crps = ContinuousRankedProbabilityScore()
    >>> crps(preds, target)
    tensor(0.7731)
```

## Quick recipe

```python
import torchmetrics.regression as _m
score = _m.ContinuousRankedProbabilityScore(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)`.