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Hands-On Ensemble Learning with R

You're reading from   Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624145
Length 376 pages
Edition 1st Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Ensemble Techniques FREE CHAPTER 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?
A. 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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