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

Continuous space and metrics


As most of this chapter's content will be dealing with trying to predict or optimize continuous variables, let's first understand how to measure the difference in a continuous space. Unless a drastically new discovery is made pretty soon, the space we live in is a three-dimensional Euclidian space. Whether we like it or not, this is the world we are mostly comfortable with today. We can completely specify our location with three continuous numbers. The difference in locations is usually measured by distance, or a metric, which is a function of a two arguments that returns a single positive real number. Naturally, the distance, , between X and Y should always be equal or smaller than the sum of distances between X and Z and Y and Z:

For any X, Y, and Z, which is also called triangle inequality. The two other properties of a metric is symmetry:

Non-negativity of distance:

Here, the metric is 0 if, and only if, X=Y. The distance is the distance as we understand it...