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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
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Author (1):
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 R. Nicolas R. Nicolas
Author Profile Icon R. Nicolas
R. Nicolas
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Table of Contents (20) Chapters Close

Scala for Machine Learning
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Hello World! 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks Basic Concepts Index

Time series in Scala


The overwhelming majority of examples used to illustrate the different machine algorithms in this book deal with time series or sequential, time-ordered set of observations.

Types and operations

The Primitives types section under Source code in Chapter 1, Getting Started, introduced the types for a time series of a single XSeries[T] variable and multiple XVSeries[T] variables.

A time series of observations is a vector (a Vector type) of observation elements of the following types:

  • A T type in the case of a single variable/feature observation

  • An Array[T] type for observations with more than one variable/feature

A time series of labels or expected values is a single variable vector for which elements may have a primitive Int type for classification and Double for regression.

A time series of labeled observations is a pair of a vector of observations and a vector of labels:

Visualization of the single features and multi-feature observations

The two generic XSeries and XVSeries types...

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