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Mastering Predictive Analytics with R

You're reading from   Mastering Predictive Analytics with R Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781783982806
Length 414 pages
Edition 1st Edition
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Toc

Table of Contents (13) Chapters Close

Preface 1. Gearing Up for Predictive Modeling 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Neural Networks 5. Support Vector Machines 6. Tree-based Methods 7. Ensemble Methods 8. Probabilistic Graphical Models 9. Time Series Analysis 10. Topic Modeling 11. Recommendation Systems Index

A little graph theory


Graph theory is a branch of mathematics that deals with mathematical objects known as graphs. Here, a graph does not have the everyday meaning that we are more used to talking about, in the sense of a diagram or plot with an x and y axis. In graph theory, a graph consists of two sets. The first is a set of vertices, which are also referred to as nodes. We typically use integers to label and enumerate the vertices. The second set consists of edges between these vertices.

Thus, a graph is nothing more than a description of some points and the connections between them. The connections can have a direction so that an edge goes from the source or tail vertex to the target or head vertex. In this case, we have a directed graph. Alternatively, the edges can have no direction, so that the graph is undirected.

A common way to describe a graph is via the adjacency matrix. If we have V vertices in the graph, an adjacency matrix is a V×V matrix whose entries are 0 if the vertex represented...

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