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  • Book Overview & Buying Mastering Predictive Analytics with R
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Mastering Predictive Analytics with R

Mastering Predictive Analytics with R

3.9 (18)
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Mastering Predictive Analytics with R

Mastering Predictive Analytics with R

3.9 (18)

Overview of this book

This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.
Table of Contents (13 chapters)
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12
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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