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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (17) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 2. Linear Regression - The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Modeling and evaluation

Modeling will be broken into two distinct parts. The first will focus on word frequency and correlation and culminate in the building of a topic model. In the next portion, we will examine many different quantitative techniques by utilizing the power of the qdap package in order to compare two different speeches.

Word frequency and topic models

As we have everything set up in the document-term matrix, we can move on to exploring word frequencies by creating an object with the column sums, sorted in descending order. It is necessary to use as.matrix() in the code to sum the columns. The default order is ascending, so putting - in front of freq will change it to descending:

    > freq <- colSums(as.matrix(dtm))

> ord <- order...
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