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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)

Fitting a generalized additive model to data

Generalized Additive Model (GAM), which is used to fit generalized additive models, can be viewed as a semiparametric extension of GLM. While GLM holds the assumption that there is a linear relationship between dependent and independent variables, GAM fits the model on account of the local behavior of data. As a result, GAM has the ability to deal with highly nonlinear relationships between dependent and independent variables. In the following recipe, we will introduce how to fit regression using a generalized additive model.

Getting ready

We need to prepare a data frame containing variables, where one of the variables is a response variable and the others may be predictor variables.

How to do it...

Perform the following steps to fit a generalized additive model into data:

First, load the mgcv package, which contains the gam function:

        > install.packages("mgcv")
        > library(mgcv)

Then, install the MASS package and load the Boston...

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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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