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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

XGBoost


XGBoost is the most popular boosting technique in recent times. Although there have been various new versions that have been developed by large corporations, XGBoost still remains the undisputed king. Let's look at a brief history of boosting.

How Does the Boosting Process Work?

Boosting differs from bagging in its core principles; the learning process is, in fact, sequential. Every model built in an ensemble is ideally an improved version of the previous model. To understand boosting in simple terms, imagine you are playing a game where you must remember all the objects placed on the table that you are shown just once for 30 seconds. The moderator of the game arranges around 50-100 different objects on a table, such as a bat, ball, clock, die, coins, and so on, and covers them with a large piece of cloth. When the game begins, he withdraws the cloth from the table and gives you exactly 30 seconds to see them and puts the curtain back. You now must recollect all the objects you can...

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