  Most automation in machine learning focuses on model selection and hyper
parameter tuning, and many overlook the challenge of automatically defining
predictive tasks. We still heavily rely on human experts to define prediction
tasks, and generate labels by aggregating raw data. In this paper, we tackle
the challenge of defining useful prediction problems on event-driven
time-series data. We introduce MLFriend to address this challenge. MLFriend
first generates all possible prediction tasks under a predefined space, then
interacts with a data scientist to learn the context of the data and recommend
good prediction tasks from all the tasks in the space. We evaluate our system
on three different datasets and generate a total of 2885 prediction tasks and
solve them. Out of these 722 were deemed useful by expert data scientists. We
also show that an automatic prediction task discovery system is able to
identify top 10 tasks that a user may like within a batch of 100 tasks.
