evalml.pipelines.TimeSeriesMulticlassClassificationPipeline

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

name = 'Time Series Multiclass Classification Pipeline'

custom_name = None

problem_type = 'time series multiclass'

Instance attributes

classes_	Gets the class names for the problem.
feature_importance	Return importance associated with each feature.
parameters	Returns parameter dictionary for this pipeline

Methods:

__init__	Machine learning pipeline for time series classification problems made out of transformers and a classifier.
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.
describe	Outputs pipeline details including component parameters
fit	Fit a time series classification 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.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives.

Class Inheritance

evalml.pipelines.TimeSeriesBinaryClassificationPipeline.score evalml.pipelines.TimeSeriesMulticlassClassificationPipeline.__init__