# ConformalResult


Result of conformal prediction.


Usage

``` python
ConformalResult(
    values,
    lower,
    upper,
    level,
    method,
    calibration_scores,
    quantile,
)
```


Returned by [ConformalPredictor.predict()](ConformalPredictor.md#whittaker.ConformalPredictor.predict). Holds point predictions together with distribution-free prediction intervals whose coverage is guaranteed (under exchangeability) to be at least the nominal `level`, regardless of whether the underlying [GAM](GAM.md#whittaker.GAM) is correctly specified.


## Attributes


`values: NDArray`  
Point predictions (response scale); for `"cv+"` and `"jackknife+"` this is the average of the fold/leave-one-out models' predictions.

`lower: NDArray`  
Lower prediction bounds.

`upper: NDArray`  
Upper prediction bounds.

`level: float`  
Nominal coverage level (e.g. `0.95`).

`method: str`  
Conformal method used (`"split"`, `"cv+"`, or `"jackknife+"`).

`calibration_scores: NDArray`  
Conformity scores (absolute residuals) from the calibration step.

`quantile: float`  
The calibration quantile used for interval width (only meaningful for the `"split"` method, where the interval is `values +/- quantile`; for `"cv+"`/`"jackknife+"` interval bounds vary per observation and are not simply `values +/- quantile`).