mean_poisson_deviance (original) (raw)
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...