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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

Defining DR


It is a most commonly accepted rule of thumb that it is difficult to understand or visualize data represented in or by more than three dimensions.

Dimensional (-ity) reduction is the process of attempting to reduce the number of random variables (or data dimensions) under statistical consideration, or perhaps better put: finding a lower-dimensional representation of the feature-set that is of interest.

This allows the data scientist to:

  • Avoid what is referred to as the curse of dimensionality

    Note

    The curse of dimensionality refers to a phenomenon that arises when attempting to analyze data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings or everyday experience.

  • Reduce the amount of time and memory required for the proper analysis of the data

  • Allow the data to be more easily visualized

  • Eliminate features irrelevant to the model's purpose

  • Reduce model noise

A useful (albeit perhaps over-used) conceptual example of...

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