get_regressor_by_name

tsml_eval.experiments.get_regressor_by_name(regressor_name, random_state=None, n_jobs=1, fit_contract=0, checkpoint=None, **kwargs)

Return a regressor matching a given input name.

Basic way of creating a regressor to build using the default or alternative settings. This set up is to help with batch jobs for multiple problems and to facilitate easy reproducibility through run_regression_experiment.

Generally, inputting a regressor class name will return said regressor with default settings.

Parameters:

regressor_namestr

String indicating which regressor to be returned.

random_stateint, RandomState instance or None, default=None

Random seed or RandomState object to be used in the regressor if available.

n_jobs: int, default=1

The number of jobs to run in parallel for both regressor fit and predict if available. -1 means using all processors.

fit_contract: int, default=0

Contract time in minutes for regressor fit if available.

checkpoint: str or None, default=None

Path to a checkpoint file to save the regressor if available. No checkpointing if None.

**kwargs

Additional keyword arguments to be passed to the regressor.