> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> ray LightGBM forecaster

# RayLGBMForecast

Wrapper of `lightgbm.ray.RayLGBMRegressor` that adds a `model_` property
that contains the fitted booster and is sent to the workers to in the
forecasting step.

***

<a href="https://github.com/Nixtla/mlforecast/blob/main/mlforecast/distributed/models/ray/lgb.py#L11" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### RayLGBMForecast

> ```text theme={null}
>  RayLGBMForecast (boosting_type:str='gbdt', num_leaves:int=31,
>                   max_depth:int=-1, learning_rate:float=0.1,
>                   n_estimators:int=100, subsample_for_bin:int=200000, obje
>                   ctive:Union[str,Callable[[Optional[numpy.ndarray],numpy.
>                   ndarray],Tuple[numpy.ndarray,numpy.ndarray]],Callable[[O
>                   ptional[numpy.ndarray],numpy.ndarray,Optional[numpy.ndar
>                   ray]],Tuple[numpy.ndarray,numpy.ndarray]],Callable[[Opti
>                   onal[numpy.ndarray],numpy.ndarray,Optional[numpy.ndarray
>                   ],Optional[numpy.ndarray]],Tuple[numpy.ndarray,numpy.nda
>                   rray]],NoneType]=None,
>                   class_weight:Union[Dict,str,NoneType]=None,
>                   min_split_gain:float=0.0, min_child_weight:float=0.001,
>                   min_child_samples:int=20, subsample:float=1.0,
>                   subsample_freq:int=0, colsample_bytree:float=1.0,
>                   reg_alpha:float=0.0, reg_lambda:float=0.0, random_state:
>                   Union[int,numpy.random.mtrand.RandomState,numpy.random._
>                   generator.Generator,NoneType]=None,
>                   n_jobs:Optional[int]=None, importance_type:str='split',
>                   **kwargs:Any)
> ```

***PublicAPI (beta):** This API is in beta and may change before
becoming stable.*