# conformal_coverage()


Compute empirical coverage of conformal intervals on held-out data.


Usage

``` python
conformal_coverage(
    predictor,
    data,
    response,
)
```


A useful sanity check that the realized coverage on a given dataset is close to (at least) the nominal `predictor.level`; systematic under-coverage may indicate a violation of the exchangeability assumption underlying conformal prediction (e.g. distribution shift between calibration and test data).


## Parameters


`predictor: ConformalPredictor`  
A fitted [ConformalPredictor](ConformalPredictor.md#whittaker.ConformalPredictor).

`data: InputData`  
Data containing both covariates and the response.

`response: str`  
Name of the response variable.


## Returns


`float`  
Fraction of observations falling within the conformal interval.