This paper analyzes the performance of multilevel logistic and support vector machine algorithms when the objective is the stratification of the sample into two groups for binary classification. Under the data simulation in Python, we show that multilevel logistic models cannot correctly classify observations under certain non-linear conditions, even when defined contextual hierarchical groups and support vector classifiers generate better predictions. Python codes are provided for replication purposes.
