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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Chapter 12. Spark Models – Rule-Based Learning

In this section, we will learn how to implement some rule-based algorithms. The method in which these algorithms can be implemented depends upon the language interface you are using and the version of Spark which is running.

For Spark 2.0, the only languages which support rule-based decision trees are Scala and Python. So in order to demonstrate how decision rules can be constructed directly in Spark, we will illustrate an example that uses Python to determine the rules for being frisked.

For other languages, such as R, there is currently no facility to run a decision tree algorithm directly on a Spark dataframe; however, there are other methods that can be used which will yield accurate trees.

We will demonstrate how to first extract a sample from Spark, download it to base R, and run our usual tools, such as rpart. Big datasets will typically contain much more data than you might need for a decision tree, so it makes perfect sense to sample appropriately...