Cracking open the black box of automated machine learning

Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how automated machine-learning systems work. The aim is to build confidence in these systems and find ways to improve them. Designing a machine-learning model for a certain task -- such as image classification, disease diagnoses, and stock market prediction -- is an arduous, time-consuming process. Experts first choose from among many different algorithms to build the model around. Then, they manually tweak "hyperparameters" -- which determine the model's overall structure -- before the model starts training.