The advent of automated machine learning platforms has expanded the access and availability of algorithmic interpretation over the past several years. But how do the different machine learning platforms stack up from a performance perspective? That’s the question that researchers from Arizona State University sought to answer.
As the market for machine learning platforms expands, users are naturally inclined to seek sources of information to rank and rate the various options that are available to them. Which systems are the easiest to use? Which ones run the fastest? Which ones give the most accurate answers? However, most of these questions have gone unanswered, according to the folks in Arizona State University’s Department of Information Systems.
Author: Alex Woodie