# CVResult


Result of [cross_validate()](cross_validate.md#whittaker.cross_validate).


Usage

``` python
CVResult(
    cv_score,
    cv_scores,
    cv_se,
    n_folds,
)
```


Holds the per-fold and aggregate out-of-sample loss values produced by k-fold cross-validation of a `~whittaker.gam.GAM` specification, along with the number of folds used to obtain them. Use `cv_score` as a single summary number for model comparison, and `cv_scores` together with `cv_se` to gauge how much that summary varies across folds.


## Parameters


`cv_score: float`  
Mean out-of-sample loss across all folds, i.e. `numpy.mean(cv_scores)`. This is on the scale of whichever `metric` was requested from [cross_validate()](cross_validate.md#whittaker.cross_validate) -- mean deviance per test observation for `metric="deviance"`, or mean squared error on the response scale for `metric="mse"`. Lower values indicate better out-of-sample predictive performance; use this value to compare competing formulas, families, or fitting methods evaluated on the same data and folds.

`cv_scores: numpy.ndarray`  
Per-fold out-of-sample loss values, shape `(n_folds,)`. Element `i` is the loss computed by fitting the GAM on every fold except `i` and scoring it on fold `i`. Inspect this array directly to check whether the CV estimate is driven by a small number of unusual folds.

`cv_se: float`  
Standard error of the mean CV score across folds, computed as the sample standard deviation of `cv_scores` (with Bessel's correction, `ddof=1`) divided by `sqrt(n_folds)`. Provides a rough measure of the uncertainty in `cv_score` due to the particular random fold assignment; useful for judging whether a difference in `cv_score` between two models is likely to be meaningful.

`n_folds: int`  
Number of folds actually requested when this result was produced. Matches the `n_folds` argument passed to [cross_validate()](cross_validate.md#whittaker.cross_validate).