evalml.pipelines.PipelineBase

class evalml.pipelines.PipelineBase(component_graph, parameters=None, custom_name=None, custom_hyperparameters=None, random_seed=0)

Base class for all pipelines.

Instance attributes

custom_hyperparameters	Custom hyperparameters for the pipeline.
custom_name	Custom name of the pipeline.
default_parameters	The default parameter dictionary for this pipeline.
feature_importance	Importance associated with each feature.
hyperparameters	Returns hyperparameter ranges from all components as a dictionary
linearized_component_graph	this is not guaranteed to be in proper component computation order
model_family	Returns model family of this pipeline template
name	Name of the pipeline.
parameters	Parameter dictionary for this pipeline
problem_type
summary	A short summary of the pipeline structure, describing the list of components used.

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
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	Build a model
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
new	Constructs a new instance of the pipeline with the same component graph but with a different set of parameters.
predict	Make predictions using selected features.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives

Class Inheritance

evalml.automl.callbacks.raise_error_callback evalml.pipelines.PipelineBase.__init__