evalml.pipelines.TimeSeriesBaselineRegressionPipeline.__init__

TimeSeriesBaselineRegressionPipeline.__init__(parameters, random_state=0)

Machine learning pipeline for time series regression problems made out of transformers and a classifier.

Required Class Variables:

component_graph (list): List of components in order. Accepts strings or ComponentBase subclasses in the list

Parameters

• parameters (dict) – Dictionary with component names as keys and dictionary of that component’s parameters as values. An empty dictionary {} implies using all default values for component parameters. Pipeline-level parameters such as gap and max_delay must be specified with the “pipeline” key. For example: Pipeline(parameters={“pipeline”: {“max_delay”: 4, “gap”: 2}}).

• random_state (int, np.random.RandomState) – The random seed/state. Defaults to 0.

evalml.pipelines.TimeSeriesBaselineRegressionPipeline evalml.pipelines.TimeSeriesBaselineRegressionPipeline.clone