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...