Feature Factory : a collaborative, crowd-sourced machine learning system

Abstract

In this thesis, I designed, implemented, and tested a machine learning learning system designed to crowd-source feature discovery called Feature Factory. Feature Factory provides a complete web-based platform for users to define, extract, and test features on any given machine learning problem. This project involved designing, implementing, and testing a proof-of-concept version of this platform. Creating the platform involved developing user-side infrastructure and system-side infrastructure. The user-side infrastructure required careful design decisions to provide users with a clear and concise interface and workflow. The system-side infrastructure involved constructing an automated feature aggregation, extraction, and testing pipeline that can be executed with a few simple commands. Testing was performed by presenting three different machine learning problems to test users via the user-side infrastructure of Feature Factory. Users were asked to write features for the three different machine learning problems as well as comment on the usability of the system. The systemside infrastructure was utilized to analyze the effectiveness and performance of the features written by the users.