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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Toc

Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

Predicting the authenticity of banknotes


In this section, we will study the problem of predicting whether a particular banknote is genuine or whether it has been forged. The banknote authentication dataset is hosted at https://archive.ics.uci.edu/ml/datasets/banknote+authentication. The creators of the dataset have taken specimens of both genuine and forged banknotes and photographed them with an industrial camera. The resulting grayscale image was processed using a type of time-frequency transformation known as a wavelet transform. Three features of this transform are constructed, and, along with the image entropy, they make up the four features in total for this binary classification task.

Column name

Type

Definition

waveletVar

Numerical

Variance of the wavelet-transformed image

waveletSkew

Numerical

Skewness of the wavelet-transformed image

waveletCurt

Numerical

Kurtosis of the wavelet-transformed image

entropy

Numerical

Entropy of the image

class

Binary

Authenticity...

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