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

Linear regression


As explained in Chapter 2, Data Pipelines and Modeling, most complex machine learning problems can be reduced to optimization as our final goal is to optimize the whole process where the machine is involved as an intermediary or the complete solution. The metric can be explicit, such as error rate, or more indirect, such as Monthly Active Users (MAU), but the effectiveness of an algorithm is finally judged by how it improves some metrics and processes in our lives. Sometimes, the goals may consist of multiple subgoals, or other metrics such as maintainability and stability might eventually be considered, but essentially, we need to either maximize or minimize a continuous metric in one or other way.

For the rigor of the flow, let's show how the linear regression can be formulated as an optimization problem. The classical linear regression needs to optimize the cumulative error rate:

Here, is the estimate given by a model, which, in the case of linear regression, is as follows...