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) np.float64(1.4260...)
