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Hands-On Ensemble Learning with R

You're reading from Hands-On Ensemble Learning with R

Product type Book

Published in Jul 2018

Publisher Packt

ISBN-13 9781788624145

Pages 376 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Prabhanjan Narayanachar Tattar

Table of Contents (17) Chapters

Hands-On Ensemble Learning with R

Contributors

Preface

1. Introduction to Ensemble Techniques

2. Bootstrapping

3. Bagging

4. Random Forests

5. The Bare Bones Boosting Algorithms

6. Boosting Refinements

7. The General Ensemble Technique

8. Ensemble Diagnostics

9. Ensembling Regression Models

10. Ensembling Survival Models

11. Ensembling Time Series Models

12. What's Next?

Bibliography

Index

Pre-processing the housing data

The dataset was selected from www.kaggle.com and the title of the project is House Prices: Advanced Regression Techniques. The main files we will be using are test.csv and train.csv, and the files are available in the companion bundle package. A description of the variables can be found in the data_description.txt file. Further details, of course, can be obtained at https://www.kaggle.com/c/house-prices-advanced-regression-techniques/. The train dataset contains 1460 observations, while the test dataset contains 1459 observations. The price of the property is known only in the train dataset and are not available for those in the test dataset. We will use the train dataset for model development only. The datasets are first loaded into an R session and a beginning inspection is done using the read.csv, dim, names, and str functions:

> housing_train <- read.csv("../Data/Housing/train.csv",
+                           row.names = 1,na.strings = "NA",
+  ...

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Authors (1)

Prabhanjan Narayanachar Tattar

Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages.

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