> ## 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 XGBoost forecaster
# RayXGBForecast
Wrapper of `xgboost.ray.RayXGBRegressor` that adds a `model_` property
that contains the fitted model and is sent to the workers in the
forecasting step.
***
source
### RayXGBForecast
> ```text theme={null}
> RayXGBForecast (objective:Union[str,xgboost.sklearn._SklObjWProto,Callabl
> e[[Any,Any],Tuple[numpy.ndarray,numpy.ndarray]],NoneType]
> ='reg:squarederror', **kwargs:Any)
> ```
*Implementation of the scikit-learn API for Ray-distributed XGBoost
regression. See :doc:`/python/sklearn_estimator` for more information.*
| | **Type** | **Default** | **Details** |
| ----------- | -------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| objective | Union | reg:squarederror | Specify the learning task and the corresponding learning objective or a custom
objective function to be used.
For custom objective, see :doc:`/tutorials/custom_metric_obj` and
:ref:`custom-obj-metric` for more information, along with the end note for
function signatures. |
| kwargs | Any | | Keyword arguments for XGBoost Booster object. Full documentation of parameters
can be found :doc:`here `.
Attempting to set a parameter via the constructor args and \*\*kwargs
dict simultaneously will result in a TypeError.
.. note:: \*\*kwargs unsupported by scikit-learn
\*\*kwargs is unsupported by scikit-learn. We do not guarantee
that parameters passed via this argument will interact properly
with scikit-learn.
.. note:: Custom objective function
A custom objective function can be provided for the `objective`
parameter. In this case, it should have the signature `objective(y_true,
y_pred) -> [grad, hess]` or `objective(y_true, y_pred, *, sample_weight)
-> [grad, hess]`:
y\_true: array\_like of shape \[n\_samples]
The target values
y\_pred: array\_like of shape \[n\_samples]
The predicted values
sample\_weight :
Optional sample weights.
grad: array\_like of shape \[n\_samples]
The value of the gradient for each sample point.
hess: array\_like of shape \[n\_samples]
The value of the second derivative for each sample point |
| **Returns** | **None** | | |