## models.log_score()


Mean log score `-(1/n) sum log f(y_i)` of the outcomes under the


Usage


``` python
models.log_score(
    family, y, mu, dispersion=1.0, weights=None, theta=None, power=None
)
```


family's predictive distribution; lower is better.


## Parameters


`family: str`  

`y: list of float`  

`mu: list of float`  

`dispersion: float = ``1.0`  

`weights: list of float = None`  

`theta: float = None`  

`power: float = None`  


## Returns


`float`  


## Examples

``` python
>>> from prospicio.models import log_score
>>> round(log_score("poisson", [0.0], [1.0]), 12)
```

1.0