evalml.pipelines.TimeSeriesBaselineRegressionPipeline

class evalml.pipelines.TimeSeriesBaselineRegressionPipeline(parameters, random_state=None, random_seed=0)

Baseline Pipeline for time series regression problems.

name = 'Time Series Baseline Regression Pipeline'

custom_name = None

summary = 'Time Series Baseline Estimator'

component_graph = ['Time Series Baseline Estimator']

problem_type = 'time series regression'

model_family = 'baseline'

hyperparameters = {'Time Series Baseline Estimator': {}}

custom_hyperparameters = None

default_parameters = {'Time Series Baseline Estimator': {'gap': 1}}

Instance attributes

feature_importance	Return importance associated with each feature.
linearized_component_graph
parameters	Returns parameter dictionary for this pipeline

Methods:

__init__	Machine learning pipeline for time series regression problems made out of transformers and a classifier.
can_tune_threshold_with_objective	Determine whether the threshold of a binary classification pipeline can be tuned.
clone	Constructs a new pipeline with the same components, parameters, and random state.
compute_estimator_features	Transforms the data by applying all pre-processing components.
create_objectives
describe	Outputs pipeline details including component parameters
fit	Fit a time series regression pipeline.
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
graph_feature_importance	Generate a bar graph of the pipeline’s feature importance
load	Loads pipeline at file path
predict	Make predictions using selected features.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives.

Class Inheritance

evalml.pipelines.MeanBaselineRegressionPipeline.score evalml.pipelines.TimeSeriesBaselineRegressionPipeline.__init__