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한국어(KO) fly51fly (@fly51fly) Michigan과 Google Research 연구진이 구조화 데이터에 대해 진화적 특성 공학을 수행하는 방법을 제안했습니다. 전통적 ML 파이프라인의 피처 엔지니어링 자동화 관점에서 흥미로운 연구입니다. https:// x.com/fly51fly/

Michigan and Google Research explore evolutionary feature engineering

By PulseAugur Editorial · [1 sources] ·

Researchers from Michigan and Google Research have proposed a method for performing evolutionary feature engineering on structured data. This work is notable for its potential to automate feature engineering within traditional machine learning pipelines. AI

RANK_REASON The cluster describes a research proposal for feature engineering in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Michigan and Google Research explore evolutionary feature engineering

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The cluster describes a research proposal for feature engineering in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — sigmoid.social TIER_1 한국어(KO) · [email protected] ·

    fly51fly (@fly51fly) proposed a method for evolutionary feature engineering on structured data by researchers from Michigan and Google Research. This is an interesting study from the perspective of automating feature engineering in traditional ML pipelines. https:// x.com/fly51fly/

    fly51fly (@fly51fly) Michigan과 Google Research 연구진이 구조화 데이터에 대해 진화적 특성 공학을 수행하는 방법을 제안했습니다. 전통적 ML 파이프라인의 피처 엔지니어링 자동화 관점에서 흥미로운 연구입니다. https:// x.com/fly51fly/status/20731557 26814609469 # featureengineering # structureddata # ml # research # google

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