evalml.pipelines.TimeSeriesRegressionPipeline.__init__¶
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TimeSeriesRegressionPipeline.__init__(component_graph, parameters=None, custom_name=None, custom_hyperparameters=None, random_seed=0)[source]¶ Machine learning pipeline for time series regression problems made out of transformers and a classifier.
- Parameters
component_graph (list or dict) – List of components in order. Accepts strings or ComponentBase subclasses in the list. Note that when duplicate components are specified in a list, the duplicate component names will be modified with the component’s index in the list. For example, the component graph [Imputer, One Hot Encoder, Imputer, Logistic Regression Classifier] will have names [“Imputer”, “One Hot Encoder”, “Imputer_2”, “Logistic Regression Classifier”]
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 date_index, gap, and max_delay must be specified with the “pipeline” key. For example: Pipeline(parameters={“pipeline”: {“date_index”: “Date”, “max_delay”: 4, “gap”: 2}}).
random_seed (int) – Seed for the random number generator. Defaults to 0.