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Machine Learning with R Cookbook, Second Edition

You're reading from Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

Product type Paperback

Published in Oct 2017

Publisher Packt

ISBN-13 9781787284395

Length 572 pages

Edition 2nd Edition

Languages

R

Tools

Hadoop

Concepts

Machine Learning

Authors (2):

Ashish Bhatia

Yu-Wei, Chiu (David Chiu)

View More author details

Table of Contents (15) Chapters

Preface

1. Practical Machine Learning with R FREE CHAPTER

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)

Different types of regression

This section will cover different types of regression:

Linear regression: This is the oldest type and most widely known type of regression. In this the dependent variable is continuous and the independent variable can be discrete or continuous and the regression line is linear. Linear regression is very sensitive to outliers and cross-correlations.
Logistic regression: This is used when the dependent variable is binary in nature (0 or 1, success or failure, survived or died, yes or no, true or false). It is widely used in clinical trials, fraud detection, and so on. It does not require there to be a linear relationship between dependent and independent variables.
Polynomial regression: This implies of polynomial equation here the power of the independent variable is more than one. In this case the regression line is not a straight line, but a curved line.
Ridge regression: This is a more robust version of linear regression and is used when data variables are highly...

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Authors (1)

Yu-Wei, Chiu (David Chiu)

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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