Models
RayXGBForecast
ray XGBoost forecaster
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
Implementation of the scikit-learn API for Ray-distributed XGBoost regression.
Type | Default | Details | |
---|---|---|---|
objective | Union | reg:squarederror | Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). |
kwargs | Any | Keyword 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 | |
Returns | None |