Cracking open the black box of automated machine learning

Recently developed automated machine-learning (AutoML) systems iteratively test and modify algorithms and those hyperparameters, and select the best-suited models. In case studies with science graduate students, who were AutoML novices, the researchers found about 85 percent of participants who used ATMSeer were confident in the models selected by the system. Results indicate three major factors — number of algorithms searched, system runtime, and finding the top-performing model — determined how users customized their AutoML searches.

Source: news.mit.edu