Rapid Feature Harvesting Using DFS & Data Engineering Techniques • Ananth Gundabattula • YOW! 2019
This presentation was recorded at YOW! 2019. #GOTOcon #YOW
Ananth Gundabattula - Senior Architect at Commonwealth Bank of Australia
ORIGINAL TALK TITLE
Auto Feature Engineering: Rapid Feature Harvesting Using DFS & Data Engineering Techniques
RESOURCES
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 summary of DFS algorithmic constructs followed by enhancements that can be done on featuretools library to enable it for many other use cases [...]
RECOMMENDED BOOKS
Stefan Helzle • Low-Code Application Development with Appian •
Phil Winder • Reinforcement Learning •
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) •
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns •
#ML #AI #ArtificialIntellignce #SoftwareEngineering #Programming #AnanthGundabattula #YOWcon
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