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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):

Yu-Wei, Chiu (David Chiu)

Ashish Bhatia

View More author details

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 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)

Using the Super Learner algorithm

Super Learner is an algorithm that uses various learning prediction models and finds the optimal combination of collection of algorithms. It learns best optimal fits. Ensemble methods use multiple learning algorithms to obtain better performance.

Getting ready

In this recipe, we will use the SuperLearner package.

How to do it...

Perform the following steps in R:

> install.packages("SuperLearner")> install.packages("caret")> install.packages("glmnet")> install.packages("xgboost")> install.packages("randomForest")> library(SuperLearner)

To list all the available wrappers for predictions and screening, execute the following:

        > listWrappers()

It will list all prediction algorithms and screening algorithms as follows:

        > data(Boston, package="MASS")
        > training = sample(nrow(Boston), 100)
        > X = Boston[training, ]
        > X_Hold = Boston[-training, ]
        > Y = Boston$medv[training, ]
        > Y_Hold...

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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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