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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
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Alexey Grigorev
Richard M. Reese Richard M. Reese
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Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
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Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Dimensionality reduction


Dimensionality reduction, as the name suggests, reduces the dimensionality of your dataset. That is, these techniques try to compress the dataset such that only the most useful information is retained, and the rest is discarded.

By dimensionality of a dataset, we mean the number of features of this dataset. When the dimensionality is high, that is, there are too many features, it can be bad due to the following reasons:

  • If there are more features than the items of the dataset, the problem becomes ill-defined and some linear models, such as ordinary least squares (OLS) regression cannot handle this case
  • Some features may be correlated and cause problems with training and interpreting the models
  • Some of the features can turn out to be noisy or irrelevant and confuse the model
  • Distances start to make less sense in high dimensions -- this problem is commonly referred to as the curse of dimensionality
  • Processing a large set of features may be computationally expensive

In the...

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