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You're reading from Mastering Predictive Analytics with R

Product type Book

Published in Jun 2015

Publisher

ISBN-13 9781783982806

Pages 414 pages

Edition 1st Edition

Languages

Concepts

Predictive Analytics

Authors (2):

Rui Miguel Forte

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Table of Contents (19) Chapters

Mastering Predictive Analytics with R

Credits

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

Preface

Gearing Up for Predictive Modeling

Linear Regression

Logistic Regression

Neural Networks

Support Vector Machines

Tree-based Methods

Ensemble Methods

Probabilistic Graphical Models

Time Series Analysis

Topic Modeling

Recommendation Systems

Index

Bagging

The focus of this chapter is on combining the results from different models in order to produce a single model that will outperform individual models on their own. Bagging is essentially an intuitive procedure for combining multiple models trained on the same data set, by using majority voting for classification models and average value for regression models. We'll present this procedure for the classification case, and later show how this is easily extended to handle regression models.

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Authors (2)

Rui Miguel Forte

Why do you think this reviewer is suitable for this book? Mr. Rui Miguel Forte has authored a book for Packt titled “Mastering Predictive Analytics with R”. The book has received a 5 star rating. He has 3 years experience as a Data Scientist. He has knowledge of Scala, Python, R, PHP. • Has the reviewer published any articles or blogs on this or a similar tool/technology ? [Provide Links and References] A brief of Unsupervised learning has been covered in his book “Mastering Predictive Analytics with R” https://www.safaribooksonline.com/library/view/mastering-predictive-analytics/9781783982806/ https://www.linkedin.com/profile/view?id=AAkAAAC5YUIBYL7LyLCWZ6LsR0ENJxByC2jU9AU&authType=NAME_SEARCH&authToken=c1Pg&locale=en_US&trk=tyah&trkInfo=clickedVertical%3Amynetwork%2CclickedEntityId%3A12149058%2CauthType%3ANAME_SEARCH%2Cidx%3A1-1-1%2CtarId%3A1444032603690%2Ctas%3ARui%20Miguel%20Forte • Feedback on the Outline (in case outline has been shared with the reviewer) The author said the outline is good to go. • Did the reviewer share any concerns or questions regarding the reviewing process? (related to the schedule, commitment, or any additional comments) No

See other products by Rui Miguel Forte

Rui Miguel Forte

Rui Miguel Forte is currently the chief data scientist at Workable. He was born and raised in Greece and studied in the UK. He is an experienced data scientist, having over 10 years of work experience in a diverse array of industries spanning mobile marketing, health informatics, education technology, and human resources technology. His projects have included predictive modeling of user behavior in mobile marketing promotions, speaker intent identification in an intelligent tutor, information extraction techniques for job applicant resumes and fraud detection for job scams. He currently teaches R, MongoDB, and other data science technologies to graduate students in the Business Analytics MSc program at the Athens University of Economics and Business. In addition, he has lectured in a number of seminars, specialization programs, and R schools for working data science professionals in Athens. His core programming knowledge is in R and Java, and he has extensive experience working with a variety of database technologies such as Oracle, PostgreSQL, MongoDB, and HBase. He holds a Master’s degree in Electrical and Electronic Engineering from Imperial College London and is currently researching machine learning applications in information extraction and natural language processing.

See other products by Rui Miguel Forte

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Bagging procedure for binary classification

Inputs:

data: The input data frame containing the input features and a column with the binary output label
M: An integer, representing the number of models that we want to train

Output:

models: A set of Μ trained binary classifier models

Method:

1. Create a random sample of size n, where n is the number of observations in the original data set, with replacement. This means that some of the observations from the original training set will be repeated and some...