evalml.preprocessing.SMOTENCCVSplit

class evalml.preprocessing.SMOTENCCVSplit(categorical_features=None, sampling_strategy='auto', n_splits=3, shuffle=True, n_jobs=- 1, random_seed=0)

Splits the data into KFold cross validation sets and uses SMOTENC to balance the training data. Keeps the validation data the same. Works on numeric and categorical data, but categorical data must be numerical

Methods

__init__	Create a TV or CV data splitter instance
get_n_splits	Returns the number of splits of this object.
split	Splits and returns the sampled training data using the data sampler provided.
transform_sample	Transforms the input data with the balancing strategy.

Class Inheritance

evalml.preprocessing.RandomUnderSamplerTVSplit.transform_sample evalml.preprocessing.SMOTENCCVSplit.__init__