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Mastering Machine Learning with R

By : Cory Lesmeister
Book Image

Mastering Machine Learning with R

By: Cory Lesmeister

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Mastering Machine Learning with R
Cory Lesmeister
Oct, 2015 | 400 pages
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Table of Contents (20 chapters)
Mastering Machine Learning with R
Mastering Machine Learning with R
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
A Process for Success
A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
2
Linear Regression – The Blocking and Tackling of Machine Learning
Linear Regression – The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
3
Logistic Regression and Discriminant Analysis
Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Model selection
Summary
4
Advanced Feature Selection in Linear Models
Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Summary
5
More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors
Support Vector Machines
Business case
Feature selection for SVMs
Summary
6
Classification and Regression Trees
Classification and Regression Trees
Introduction
An overview of the techniques
Business case
Summary
7
Neural Networks
Neural Networks
Neural network
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
8
Cluster Analysis
Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Data understanding and preparation
Modeling and evaluation
Summary
9
Principal Components Analysis
Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
10
Market Basket Analysis and Recommendation Engines
Market Basket Analysis and Recommendation Engines
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
Business understanding and recommendations
Data understanding, preparation, and recommendations
Modeling, evaluation, and recommendations
Summary
11
Time Series and Causality
Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
12
Text Mining
Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary
R Fundamentals
R Fundamentals
Introduction
Getting R up and running
Using R
Data frames and matrices
Summary stats
Installing and loading the R packages
Summary
Index
Index

Modeling and evaluation

For the modeling and evaluation step, we will focus on three tasks. The first is to produce a univariate forecast model applied to just the surface temperature. The second is developing a regression model of the surface temperature based on itself and carbon emissions. Finally, we will try and discover if emissions 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 a Holt linear trend model or an ARIMA model. As discussed previously, the temperature anomalies start to increase around 1970. Therefore, I recommend looking at it from this point to the present. The following code creates the subset and plots the series:

> T2 = window(T, start=1970)

> plot(T2)

Our train and test sets will be through 2007, giving us eight years of data to evaluate for the selection. Once again, the window() function allows us to...

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