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Ensemble learning for AI developers learn bagging, stacking, and boosting methods with use cases

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 baggin...

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Bibliographic Details
Main Authors: Kumar, Alok, Jain, Mayank (Author)
Format: eBook
Language:English
Published: Berkeley, CA Apress 2020
Subjects:
Artificial Intelligence
Computers / Programming Languages / Python / Bisacsh
Computer Programming / Fast
Artificial Intelligence / Aat
Artificial Intelligence / Fast
Python (langage De Programmation)
Computer Programming / Http://id.loc.gov/authorities/subjects/sh85107310
Computer Programming / Aat
Open Source Software / Http://id.loc.gov/authorities/subjects/sh99003437
Python (computer Program Language) / Fast
Computers / Programming / Open Source / Bisacsh
Open Source Software / Fast
Intelligence Artificielle
Ensemble Learning (machine Learning) / Http://id.loc.gov/authorities/subjects/sh2020008824
Artificial Intelligence / Bicssc
Artificial Intelligence / Http://id.loc.gov/authorities/subjects/sh85008180
Programming & Scripting Languages: General / Bicssc
Programmation (informatique)
Python (computer Program Language) / Http://id.loc.gov/authorities/subjects/sh96008834
Computer Programming / Software Development / Bicssc
Logiciels Libres
Ensemble Learning (machine Learning) / Fast
Computers / Intelligence (ai) & Semantics / Bisacsh
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 1: Why Ensemble Techniques Are Needed
  • Chapter 2: Mix Training Data
  • Chapter 3: Mix Models
  • Chapter 4: Mix Combinations
  • Chapter 5: Use Ensemble Learning Libraries
  • Chapter 6: Tips and Best Practices.-
  • Includes bibliographical references and index

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