evalml.pipelines.ModeBaselineBinaryPipeline

class evalml.pipelines.ModeBaselineBinaryPipeline(parameters, random_seed=0)

Mode Baseline Pipeline for binary classification.

name = 'Mode Baseline Binary Classification Pipeline'

custom_name = 'Mode Baseline Binary Classification Pipeline'

summary = 'Baseline Classifier'

component_graph = ['Baseline Classifier']

problem_type = 'binary'

model_family = 'baseline'

hyperparameters = {'Baseline Classifier': {}}

custom_hyperparameters = {'strategy': ['mode']}

default_parameters = {'Baseline Classifier': {'strategy': 'mode'}}

Instance attributes

classes_	Gets the class names for the problem.
feature_importance	Return importance associated with each feature.
linearized_component_graph
parameters	Returns parameter dictionary for this pipeline
threshold	Threshold used to make a prediction.

Methods:

__init__	Machine learning classification pipeline 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	Build a classification model. For string and categorical targets, classes are sorted
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
optimize_threshold	Optimize the pipeline threshold given the objective to use.
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on objectives

Class Inheritance

evalml.pipelines.BaselineMulticlassPipeline.score evalml.pipelines.ModeBaselineBinaryPipeline.__init__