The Synthetic Minority Over-sampling Technique (SMOTE) node provides an over-sampling algorithm to deal with imbalanced data sets. It provides an advanced method for balancing data. The SMOTE node in Watson Studio is implemented in Python and requires the imbalanced-learn© Python library.
For details about the imbalanced-learn library, see http://contrib.scikit-learn.org/imbalanced-learn/about.html1.
The Modeling tab on the nodes palette contains the SMOTE node and other Python nodes.
1Lemaître, Nogueira, Aridas. "Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning." Journal of Machine Learning Research, vol. 18, no. 17, 2017, pp. 1-5. (http://jmlr.org/papers/v18/16-365.html)