Mean Poisson Deviance

mean-poisson-deviance

Metric mean_poisson_deviance from scikit-learn (sklearn.metrics.mean_poisson_deviance)

When to invoke this skill

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

Reference signature

from sklearn.metrics import mean_poisson_deviance

# mean_poisson_deviance(y_true, y_pred, *, sample_weight=None)

Library docstring

Mean Poisson deviance regression loss.

Poisson deviance is equivalent to the Tweedie deviance with
the power parameter `power=1`.

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

Parameters
----------
y_true : array-like of shape (n_samples,)
    Ground truth (correct) target values. Requires y_true >= 0.

y_pred : array-like of shape (n_samples,)
    Estimated target values. Requires y_pred > 0.

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

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

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

Quick recipe

import sklearn.metrics as _m
score = _m.mean_poisson_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).