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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

Classification Techniques for Supervised Learning


To approach a supervised classification algorithm, we first need to understand the basic functioning of the algorithm, explore a bit of the math in an abstract way, and then develop the algorithm using readily available packages in R. We will cover a few basic algorithms, such as white-box algorithms such as Logistic Regression and Decision Trees, and then we will move on to advanced modeling techniques, such as black-box models such as Random Forest, XGBoost, and neural networks. The list of algorithms we plan to cover is not exhaustive, but these five algorithms will help you gain a broad understanding of the topic.

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