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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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788624145

Length 376 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (15) Chapters

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

R package references

Index

Chapter 9. Ensembling Regression Models

Chapters 3, Bagging, to Chapters 8, Ensemble Diagnostics, were devoted to learning different types of ensembling methods. The discussion was largely based on the classification problem. If the regressand/output of the supervised learning problem is a numeric variable, then we have a regression problem, which will be addressed here. The housing price problem is selected for demonstration purposes throughout the chapter, and the dataset is chosen from a Kaggle competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/. The data consists of numerous variables, including as many as 79 independent variables, with the price of the house as the output/dependent variable. The dataset needs some pre-processing as some variables have missing dates, some variables have lots of levels, with a few of them only occurring very rarely, and some variables have missing data in more than 20% of observations.

The pre-processing techniques...

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Authors (1)

Tattar

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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