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

 RayXGBForecast (objective:Union[str,Callable[[numpy.ndarray,numpy.ndarray
                 ],Tuple[numpy.ndarray,numpy.ndarray]],NoneType]='reg:squa
                 rederror', **kwargs:Any)

Implementation of the scikit-learn API for Ray-distributed XGBoost regression.

TypeDefaultDetails
objectiveUnionreg:squarederrorSpecify the learning task and the corresponding learning objective or
a custom objective function to be used (see note below).
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, y_pred) -> grad, hess:

y_true: array_like of shape [n_samples]
The target values
y_pred: array_like of shape [n_samples]
The predicted values

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