## models.random_search()


Draws [n](distributions.Binomial.md#prospicio.distributions.Binomial.n) candidates with `draw(rng)` from `random.Random(seed)`


Usage

``` python
models.random_search(
    n, seed, draw, make, design, y, splits, score, n_jobs=None
)
```


and scores them as [grid_search](models.grid_search.md#prospicio.models.grid_search) does: with several hyperparameters, random candidates cover each range better than a grid of the same size.


## Parameters


`n: int`  

`seed: int`  

`draw: callable`  
`draw(rng) -> candidate`, for example `lambda rng: log_uniform(rng, 1e-4, 1.0)`.

`make`  
As [grid_search](models.grid_search.md#prospicio.models.grid_search).

`design`  
As [grid_search](models.grid_search.md#prospicio.models.grid_search).

`y`  
As [grid_search](models.grid_search.md#prospicio.models.grid_search).

`splits`  
As [grid_search](models.grid_search.md#prospicio.models.grid_search).

`score`  
As [grid_search](models.grid_search.md#prospicio.models.grid_search).

`n_jobs`  
As [grid_search](models.grid_search.md#prospicio.models.grid_search).


## Returns


`SearchResult`