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AssigneeDell Products L.P.
InventorsRobert Anthony Lincourt, JR., Stephen James Todd, Eloy Francisco Macha
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A method and system for metadata-based feature expansion for automated machine learning processes. In machine learning, feature selection—or a method through model inputs are reduced in dimensionality by retaining the relevant features, while also discarding the noise, thereof—often plays a pivotal role in the availability of algorithm(s) to derive model(s) from, the optimizing of said model(s) through training and/or testing, and, ultimately, the reaching of acceptable performance thresholds evaluating said model(s), which leads to their implementation in solving real-world problems. Embodiments disclosed herein, accordingly, leverage captured dataset metadata, as well as graph techniques, to identify and suggest one or more supplemental features distinct from original features for, and yet relevant to, the real-world problem and any machine learning models being evaluated.
A method for processing feature queries, the method comprising:
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A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor, enables the computer processor to perform a method for processing feature queries, the method comprising:
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A system, the system comprising:
Dependencies are inferred from the claim text.