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As machine learning adoption permeates across many business models, so is the need to deliver models at a much faster rate. Feature engineering 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.
Deep Feature Synthesis (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 summary of DFS algorithmic constructs followed by enhancements that can be done on featuretools library to enable it for many other use cases
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