Book Image

Mastering Scala Machine Learning

By : Kozlov
Book Image

Mastering Scala Machine Learning

By: 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 (12 chapters)
10
10. Advanced Model Monitoring
11
Index

Classification metrics


If the label is discrete, the prediction problem is called classification. In general, the target can take only one of the values for each record (even though multivalued targets are possible, particularly for text classification problems to be considered in Chapter 6, Working with Unstructured Data).

If the discrete values are ordered and the ordering makes sense, such as Bad, Worse, Good, the discrete labels can be cast into integer or double, and the problem is reduced to regression (we believe if you are between Bad and Worse, you are definitely farther away from being Good than Worse).

A generic metric to optimize is the misclassification rate is as follows:

However, if the algorithm can predict the distribution of possible values for the target, a more general metric such as the KL divergence or Manhattan can be used.

KL divergence is a measure of information loss when probability distribution is used to approximate probability distribution :

It is closely related...