Mean Tweedie Deviance

---
name: mean-tweedie-deviance
description: Compute the mean_tweedie_deviance metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_tweedie_deviance, or asks how to score with mean_tweedie_deviance.
metadata:
  skill_kind: metric
  source_lib: scikit-learn
  import_path: sklearn.metrics.mean_tweedie_deviance
  source: library_introspection
---

# mean-tweedie-deviance

> Metric `mean_tweedie_deviance` from `scikit-learn` (sklearn.metrics.mean_tweedie_deviance)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with mean_tweedie_deviance, or
mentions `sklearn.metrics.mean_tweedie_deviance` directly, or wants the standard scikit-learn implementation.

## Reference signature

```python
from sklearn.metrics import mean_tweedie_deviance

# mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)
```

## Library docstring

```
Mean Tweedie deviance regression loss.

Read more in the :ref:`User Guide <mean_tweedie_deviance>`.

Parameters
----------
y_true : array-like of shape (n_samples,)
    Ground truth (correct) target values.

y_pred : array-like of shape (n_samples,)
    Estimated target values.

sample_weight : array-like of shape (n_samples,), default=None
    Sample weights.

power : float, default=0
    Tweedie power parameter. Either power <= 0 or power >= 1.

    The higher `p` the less weight is given to extreme
    deviations between true and predicted targets.

    - power < 0: Extreme stable distribution. Requires: y_pred > 0.
    - power = 0 : Normal distribution, output corresponds to
      mean_squared_error. y_true and y_pred can be any real numbers.
    - power = 1 : Poisson distribution. Requires: y_true >= 0 and
      y_pred > 0.
    - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0
      and y_pred > 0.
    - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
    - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0
      and y_pred > 0.
    - otherwise : Positive stable distribution. Requires: y_true > 0
      and y_pred > 0.

Returns
-------
loss : float
    A non-negative floating point value (the best value is 0.0).

Examples
--------
>>> from sklearn.metrics import mean_tweedie_deviance
>>> y_true = [2, 0, 1, 4]
>>> y_pred = [0.5, 0.5, 2., 2.]
>>> mean_tweedie_deviance(y_true, y_pred, power=1)
1.4260...
```

## Quick recipe

```python
import sklearn.metrics as _m
score = _m.mean_tweedie_deviance(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)`.