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Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
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Author (1):
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Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
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Toc

Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

Ensemble learning methods

Ensemble, in general, means a group of things that are usually seen as a whole rather than in terms of the value as against the individual value. Ensembles follow a divide-and-conquer approach used to improve performance.

Ensemble learning methods

We will start understanding the specific algorithm with an introduction to the famous concept of the wisdom of the crowd.

The wisdom of the crowd

Imperfect judgments when aggregated in a right way result in a collective intelligence, thus resulting in a superior outcome. The wisdom of the crowd is all about this collective intelligence.

In general, the term crowd is usually associated with irrationality and the common perception that there is some influence, which sways the behavior of the crowd in the context of mobs and cults. However, the fact is that this need not always be negative and works well when working with collating intellect. The key concept of Wisdom of Crowds is that the decisions made by a group of people are always robust and accurate...

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