  Attempts to incorporate topological information in supervised learning tasks
have resulted in the creation of several techniques for vectorizing persistent
homology barcodes. In this paper, we study thirteen such methods. Besides
describing an organizational framework for these methods, we comprehensively
benchmark them against three well-known classification tasks. Surprisingly, we
discover that the best-performing method is a simple vectorization, which
consists only of a few elementary summary statistics. Finally, we provide a
convenient web application which has been designed to facilitate exploration
and experimentation with various vectorization methods.
