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

Sequential trials and dealing with risk


What if my preferences for making an extra few dollars outweigh the risk of losing the same amount? I will stop on why one's preferences might be asymmetric in a little while in this section, and there is scientific evidence that this asymmetry is ingrained in our minds for evolutionary reasons, but you are right, I have to optimize the expected value of the asymmetric function of the parameterized utility now, as follows:

Why would an asymmetric function surface in the analysis? One example is repeated bets or re-investments, also known as the Kelly Criterion problem. Although originally, the Kelly Criterion was developed for a specific case of binary outcome as in a gambling machine and the optimization of the fraction of money to bet in each round (A New Interpretation of Information Rate, Bell System Technical Journal 35 (4): 917–926, 1956), a more generic formulation as an re-investment problem involves a probabilistic distribution of possible...