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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 2. Linear Regression - The Blocking and Tackling of Machine Learning FREE CHAPTER 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

Time Series and Causality

"An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today."
                                                                                                        - Laurence J. Peter

A univariate time series is where the measurements are collected over a standard measure of time, which could be by the minute, hour, day, week, or month. What makes the time series problematic over the other data is that the order of the observations probably matters. This dependency of order can cause the standard analysis methods to produce an unnecessarily high bias or variance.

It seems...

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