The `fit` method in the python imblearn.over_sampling.SMOTE package is used to train the SMOTE algorithm on a given dataset. It takes the input data as a parameter and generates synthetic samples to balance the classes in the dataset. This method learns the underlying patterns in the data distribution and uses them to create new synthetic samples for the minority class. By fitting the SMOTE algorithm, the dataset can be oversampled, improving the balance between the minority and majority classes.
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