Book Image

Scala Machine Learning Projects

Book Image

Scala Machine Learning Projects

Overview of this book

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.
Table of Contents (17 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

LR for churn prediction


LR is one of the most widely used classifiers to predict a binary response. It is a linear ML method, as described in Chapter 1, Analyzing Insurance Severity Claim. The loss function is the formulation given by the logistic loss:

For the LR model, the loss function is the logistic loss. For a binary classification problem, the algorithm outputs a binary LR model such that, for a given new data point, denoted by x, the model makes predictions by applying the logistic function:

In the preceding equation, z = WTX and if f(WTX)>0.5, the outcome is positive; otherwise, it is negative.

Note

Note that the raw output of the LR model, f(z), has a probabilistic interpretation.

Note that compared to linear regression, logistic regression provides you with a higher classification accuracy. Moreover, it is a flexible way to regularize a model for custom adjustment, and overall, the model responses are measures of probability.

Most importantly, whereas linear regression can predict...