Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Support vector machines

We have already seen some examples in which we use a straight line to separate classes.

As the dimensionality, or feature space, of a model increases, there may be many different ways to separate classes, in both linear and non-linear ways.

In the cases of support vector machines, data is first transformed into a higher dimensional space using a mapping function known as a kernel, and an optimal hyperplane is used to segment the higher dimensional space. A hyperplane uses one dimension less than the space it is trying to measure, so a straight line is used to segment a two-dimensional space, and a 2-dimensional sheet of paper is used to segment a three-dimensional space. The hyperplane can be either linear or non-linear.

Hyperplanes use support vectors which are important training tuples and are used to define the boundaries of each class. They are the most critical points in the data, and they are the most important points used which support the definition of the hyperplane...