Deep Feature Synthesis

Towards automating data science endeavors

Deep Feature Synthesis

Authors: James Max Kanter, Kalyan Veeramachaneni

Published in: IEEE International Conference on Data Science and Advanced Analytics 2015

In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically. To achieve this automation, we first propose and develop the Deep Feature Synthesis algorithm for automatically generating features for relational datasets. The algorithm follows relationships in the data to a base field, and then sequentially applies mathematical functions along that path to create the final feature. Second, we implement a generalizable machine learning pipeline and tune it using a novel Gaussian Copula process based approach. We entered the Data Science Machine in 3 data science competitions…