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In this paper, we present a data science automation system called PredictionFactory. The system uses several key automation algorithms to enable datascientists to rapidly develop predictive models and share them with domainexperts. To assess the system's impact, we implemented 3 different interfacesfor creating predictive modeling projects: baseline automation, fullautomation, and optional automation. With a dataset of online grocery shopperbehaviors, we divided data scientists among the interfaces to specifyprediction problems, learn and evaluate models, and write a report for domainexperts to judge whether or not to fund to continue working on. In total, 22data scientists created 94 reports that were judged 296 times by 26 experts. Ina head-to-head trial, reports generated utilizing full data science automationinterface reports were funded 57.5% of the time, while the ones that usedbaseline automation were only funded 42.5% of the time. An intermediateinterface which supports optional automation generated reports were funded58.6% more often compared to the baseline. Full automation and optionalautomation reports were funded about equally when put head-to-head. Theseresults demonstrate that Prediction Factory has implemented a critical amountof automation to augment the role of data scientists and improve businessoutcomes.

Prediction Factory: automated development and collaborative evaluation of predictive models