Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Influence diagrams


While the decision making process can have multiple facets, a book about decision making under uncertainty would be incomplete without mentioning influence diagrams (Influence Diagrams for Team Decision Analysis, Decision Analysis 2 (4): 207–228), which help the analysis and understanding of the decision-making process. The decision may be as mundane as selection of the next news article to show to a user in a personalized environment or a complex one as detecting malware on an enterprise network or selecting the next research project.

Depending on the weather she can try and go on a boat trip. We can represent the decision-making process as a diagram. Let's decide whether to take a river boat tour during her stay in Portland, Oregon:

Figure 02-1. A simple vacation influence diagram to represent a simple decision-making process. The diagram contains decision nodes such as Vacation Activity, observable and unobservable information nodes such as Weather Forecast and Weather...