DashAI.back.converters.RandomUnderSamplerConverter

class RandomUnderSamplerConverter(**kwargs)[source]

Methods

`__init__`(**kwargs)
`changes_row_count`()	Indicates if the converter changes the number of rows in the dataset.
`fit`(x, y)	Fit the sampler using imbalanced-learn's fit_resample and store the combined result.
`fit_resample`(X, y, **params)	Resample the dataset.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata`()	Get metadata values for the current converter.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_schema`()	Generates the component related Json Schema.
`set_fit_request`(*[, x])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_transform_request`(*[, x])	Configure whether metadata should be requested to be passed to the `transform` method.
`transform`(x[, y])	Return the stored resampled dataset (X and y combined).
`validate_and_transform`(raw_data)	It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Attributes