evalml.pipelines.components.Undersampler

class evalml.pipelines.components.Undersampler(sampling_ratio=0.25, sampling_ratio_dict=None, min_samples=100, min_percentage=0.1, random_seed=0, **kwargs)

Random undersampler component. This component is only run during training and not during predict.

name = 'Undersampler'

model_family = 'none'

hyperparameter_ranges = {}

default_parameters = {'min_percentage': 0.1, 'min_samples': 100, 'sampling_ratio': 0.25, 'sampling_ratio_dict': None}

Instance attributes

needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Initializes an undersampling transformer to downsample the majority classes in the dataset.
clone	Constructs a new component with the same parameters and random state.
describe	Describe a component and its parameters
fit	Resample the data using the sampler.
fit_transform	Fit and transform the data using the undersampler.
load	Loads component at file path
save	Saves component at file path
transform	No transformation needs to be done here.

Class Inheritance

evalml.pipelines.components.PolynomialDetrender.transform evalml.pipelines.components.Undersampler.__init__