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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

A Brief History of the Pre-Docker Era


Before diving deep into the Docker tool, let's understand some background and history.

The challenge of deploying an application in an environment-agnostic framework was achieved earlier using virtualization, that is, the entire application, dependencies, libraries, necessary frameworks, and the operating system itself was virtualized and packaged as a solution that could be deployed on a host. Multiple virtual environments could run on an infrastructure (called a hypervisor), and applications became environment-agnostic. However, this approach has a major trade-off. Packaging the entire operating system into the virtual machine (VM) of an application made the package heavy and often resulted in wasting memory and computing resources.

A more intuitive approach to this problem was to exclude the operating system from the package and only include the application-related libraries and dependencies. Additionally, enable a mechanism such that the package becomes...

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