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.

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RayXGBForecast

 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.

TypeDefaultDetails
objectiveUnionreg:squarederrorSpecify 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.
kwargsAnyKeyword arguments for XGBoost Booster object. Full documentation of parameters
can be found :doc:here </parameter>.
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,<br/> y_pred) -> [grad, hess] or objective(y_true, y_pred, *, sample_weight)<br/> -> [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
ReturnsNone
RayLGBMForecastSparkLGBMForecast