Deep Feature Synthesis: Towards Automating Data Science Endeavors

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 that featured 906 other data science teams.

Our approach beats 615 teams in these data science competitions.

In 2 of the 3 competitions we beat a majority of competitors, and in the third, we achieved 94% of the best competitor’s score.

In the best case, with an ongoing competition, we beat 85.6% of the teams and achieved 95.7% of the top submissions score.

Introduction
Data science consists of deriving insights, knowledge, and predictive models from data. This endeavor includes cleaning and curating at one end and dissemination of results at the other, and data collection and assimilation may also be involved.

After the successful development and proliferation of systems and software that are able to efficiently store, retrieve, and process data, attention has now shifted to analytics, both predictive and correlative.

Our goal is to make these endeavors more efficient, enjoyable, and successful.