Designing machine learning workflows with an application to topological data analysis

Eric Cawi, Patricio S. la Rosa, Arye Nehorai

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this paper we define the concept of the Machine Learning Morphism (MLM) as a fundamental building block to express operations performed in machine learning such as data preprocessing, feature extraction, and model training. Inspired by statistical learning, MLMs are morphisms whose parameters are minimized via a risk function. We explore operations such as composition of MLMs and when sets of MLMs form a vector space. These operations are used to build a machine learning workflow from data preprocessing to final task completion. We examine the Mapper Algorithm from Topological Data Analysis as an MLM, and build several workflows for binary classification incorporating Mapper on Hospital Readmissions and Credit Evaluation datasets. The advantage of this framework lies in the ability to easily build, organize, and compare multiple workflows, and allows joint optimization of parameters across multiple steps in an application.

Original languageEnglish
Article numbere0225577
JournalPloS one
Issue number12
StatePublished - Dec 1 2019


Dive into the research topics of 'Designing machine learning workflows with an application to topological data analysis'. Together they form a unique fingerprint.

Cite this