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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd 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 (16) Chapters Close

Preface 1. Preparing and Understanding Data 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

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, month, and so on. What makes the time series problematic over other data is that the order of the observations matters. This dependency of order can cause standard analysis methods to produce an unnecessarily high bias or variance.

It seems that there's a paucity of literature on machine learning and time series data or it's substandard. For example, I was at a data science conference in the spring of 2018, and a highly regarded machine learning expert mentioned that vector autoregression requires the data to be stationary. We'll discuss this later. When...

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