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R Machine Learning Projects

You're reading from   R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Length 334 pages
Edition 1st Edition
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Author (1):
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Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Author Profile Icon Dr. Sunil Kumar Chinnamgari
Dr. Sunil Kumar Chinnamgari
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Table of Contents (12) Chapters Close

Preface 1. Exploring the Machine Learning Landscape FREE CHAPTER 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
11. Other Books You May Enjoy

Boosting

A weak learner is an algorithm that performs relatively poorly—generally, the accuracy obtained with the weak learners is just above chance. It is often, if not always, observed that weak learners are computationally simple. Decision stumps or 1R algorithms are some examples of weak learners. Boosting converts weak learners into strong learners. This essentially means that boosting is not an algorithm that does the predictions, but it works with an underlying weak ML algorithm to get better performance.

A boosting model is a sequence of models learned on subsets of data similar to that of the bagging ensembling technique. The difference is in the creation of the subsets of data. Unlike bagging, all the subsets of data used for model training are not created prior to the start of the training. Rather, boosting builds a first model with an ML algorithm that...

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