Auto-tuning data science--new research streamlines machine learning

The tremendous recent growth of data science--both as a discipline and an application--can be attributed, in part, to its robust problem-solving power: It can predict when credit card transactions are fraudulent, help business owners figure out when to send coupons in order to maximize customer response, or facilitate educational interventions by forecasting when a student is on the cusp of dropping out. To get to these data-driven solutions, though, data scientists must shepherd their raw data through a complex series of steps, each one requiring many human-driven decisions. The last step in the process, deciding on a modeling technique, is particularly crucial. There are hundreds of techniques to choose from--from neural networks to support vector machines--and selecting the best one can mean millions of dollars of additional revenue, or the difference between spotting a flaw in critical medical devices and missing it. In a paper called "ATM: A distributed, collaborative, scalable system for automated machine learning," which was presented last week at the IEEE International Conference on Big Data, researchers from MIT and Michigan State University present a new system that automates the model selection step, even improving on human performance.