> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/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.

### `RayLGBMForecast`

Bases: <code>[RayLGBMRegressor](#lightgbm_ray.RayLGBMRegressor)</code>

#### `RayLGBMForecast.model_`

```python theme={null}
model_
```


## Related topics

- [RayXGBForecast](/mlforecast/distributed.models.ray.xgb.html.md)
- [Forecasting at Scale using ETS and ray (M5)](/statsforecast/docs/experiments/ets_ray_m5.html.md)
- [Tune NeuralForecast models with Ray Tune or Optuna](/neuralforecast/docs/capabilities/hyperparameter_tuning.html.md)
- [Quick start (distributed)](/mlforecast/docs/getting-started/quick_start_distributed.html.md)
- [Ray](/statsforecast/docs/distributed/ray.html.md)