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Machine Learning with R Cookbook, Second Edition - Second Edition

You're reading from Machine Learning with R Cookbook, Second Edition - Second Edition

Product type Book

Published in Oct 2017

Publisher Packt

ISBN-13 9781787284395

Pages 572 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Yu-Wei, Chiu (David Chiu)

Table of Contents (21) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Practical Machine Learning with R

2. Data Exploration with Air Quality Datasets

3. Analyzing Time Series Data

4. R and Statistics

5. Understanding Regression Analysis

6. Survival Analysis

7. Classification 1 - Tree, Lazy, and Probabilistic

8. Classification 2 - Neural Network and SVM

9. Model Evaluation

10. Ensemble Learning

11. Clustering

12. Association Analysis and Sequence Mining

13. Dimension Reduction

14. Big Data Analysis (R and Hadoop)

Introduction

Regression is a supervised learning method, which is employed to model and analyze the relationship between a dependent (response) variable and one or more independent (predictor) variables. One can use regression to build a prediction model, which can first be used to find the best fitted model with minimum squared errors of the fitted values. The fitted model can then be further applied to data for continuous value predictions.

There are many types of regression. If there is only one predictor variable, and the relationship between the response variable and independent variable is linear, we can apply a linear model. However, if there is more than one predictor variable, a multiple linear regression method should be used. When the relationship is nonlinear, one can use a nonlinear model to model the relationship between the predictor and response variables.

In this chapter, we will introduce how to fit a linear model into data with the lm function. Next, for distribution in...

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Authors (1)

Yu-Wei, Chiu (David Chiu)

Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at www.ywchiu.com. **********************************Acknowledgement************************************** I have immense gratitude for my family and friends for supporting and encouraging me to complete this book. I would like to sincerely thank my mother, Ming-Yang Huang (Miranda Huang); my mentor, Man-Kwan Shan; the proofreader of this book, Brendan Fisher; Members of LargitData; Data Science Program (DSP); and other friends who have offered their support.

See other products by Yu-Wei, Chiu (David Chiu)

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