mean_poisson_deviance#

sklearn.metrics.mean_poisson_deviance(y_true, y_pred, *, sample_weight=None)[source]#

Mean Poisson deviance regression loss.

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

Read more in the User Guide.

Parameters:
y_truearray-like of shape (n_samples,)

Ground truth (correct) target values. Requires y_true >= 0.

y_predarray-like of shape (n_samples,)

Estimated target values. Requires y_pred > 0.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
lossfloat

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...