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Ensemble learning for Ai developers: Learn bagging, stacking, and boosting methods with use cases / Alok Kumar

By: Contributor(s): Language: English Publication details: Apress 2020Edition: 1st edDescription: 136 p. 23 cmISBN:
  • 9781484276204
Subject(s): DDC classification:
  • 006.31 KUM;1
Summary: Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects.
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Item type Current library Call number Status Barcode
Reference Reference Anna Centenary Library 3RD FLOOR, A WING 006.31 KUM (Browse shelf(Opens below)) Not for loan 679099
Reference Reference Anna Centenary Library 3RD FLOOR, A WING 006.31 KUM;1 (Browse shelf(Opens below)) Not for loan 679100

Includes index

Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects.

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