Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16 to 24 data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost.
Data Science Machine (DSM) @cite_10 is the first system that automates feature engineering from a database of multiple tables. This feature engineering approach is based on an assumption that, for a given relational database, data scientists usually search for features via: 1. generating SQL queries to collect data for each example in the training set and 2. transforming the data into features. DSM automates the given two steps via creating an entity graph and performing automatic SQL query generation to join the tables along different paths of the entity graph. It converts the collected results into features using a predefined set of simple aggregation functions.
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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 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.