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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

Modeling and evaluation

For the modeling and evaluation step, we'll focus on three tasks. The first is to produce a univariate forecast model applied to just the surface temperature. The second is to develop a vector autoregression model of the surface temperature and CO2 levels, using that output to inform our work on whether CO2 levels Granger-cause the surface temperature anomalies.

Univariate time series forecasting

With this task, the objective is to produce a univariate forecast for the surface temperature, focusing on choosing either an exponential smoothing model, an ARIMA model, or an ensemble of methods, including a neural net. We'll train the models and determine their predictive accuracy on an out-of...

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