Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We develop an approach to learn parallel sorting algorithm classes from performance data directly in order to classify parallel sorting algorithms without using the source code. In this paper, we focus on the workflow and interfaces we developed for collecting, processing, and modeling performance data using Caliper, Thicket, PyTorch, and Scikit-learn. Our workflow results in classification accuracy of our machine learning models up to 95.3% across five different algorithm classes.