Rapid Feature Harvesting Using DFS & Data Engineering Techniques • Ananth Gundabattula • YOW! 2019

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This presentation was recorded at YOW! 2019. #GOTOcon #YOW https://yowcon.com Ananth Gundabattula - Senior Architect at Commonwealth Bank of Australia ORIGINAL TALK TITLE Auto Feature Engineering: Rapid Feature Harvesting Using DFS & Data Engineering Techniques RESOURCES https://twitter.com/edith_h https://www.linkedin.com/in/edithharbaugh https://github.com/eharbaugh ABSTRACT As #MachineLearning adoption permeates across many business models, so is the need to deliver models at a much faster rate. #FeatureEngineering arguably is one of the core foundations of model development cycle. While approaches like deep learning tend to take a different approach to feature engineering, it might not be exaggerating to say that feature engineering is the core construct which can make or break a classical machine learning model. Automating feature engineering would immensely shorten the time to market classical machine learning models. #DeepFeatureSynthesis (#DFS) is an algorithm that is implemented in the FeatureTools python package. DFS helps in rapid harvesting of new features by taking a stacking approach on top of a relational data model. DFS also has first class support for time dimensions as a fundamental construct. Some of these factors make the feature tools package a compelling tool/library for data practitioners. However the base algorithm itself can be enriched in multiple ways to make it truly appealing for many other use cases. This session will present a high level s

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