get_forecaster_by_name

tsml_eval.experiments.get_forecaster_by_name(forecaster_name, random_state=None, n_jobs=1, **kwargs)

Return a forecaster matching a given input name.

Basic way of creating a forecaster 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 for use with run_forecasting_experiment.

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

todo

Parameters:

forecaster_namestr

String indicating which classifier to be returned.

random_stateint, RandomState instance or None, default=None

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

n_jobs: int, default=1

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