# StackingResult


Result of stacking weight optimization.


Usage

``` python
StackingResult(
    weights,
    elpd_stacking,
    se_elpd_stacking,
    n_models,
    n_obs,
    method,
)
```


## Attributes


`weights: NDArray`  
Optimal stacking weights, shape `(K,)`, summing to 1. Each weight is the contribution of the corresponding model to the predictive mixture.

`elpd_stacking: float`  
Combined ELPD of the stacking mixture, summed over observations.

`se_elpd_stacking: float`  
Approximate standard error of `elpd_stacking`.

`n_models: int`  
Number of models in the comparison.

`n_obs: int`  
Number of observations.

`method: str`  
Whether the pointwise ELPD values came from `"loo"` or `"waic"`.