Book Image

Machine Learning with Scala Quick Start Guide

By : Md. Rezaul Karim, Ajay Kumar N
Book Image

Machine Learning with Scala Quick Start Guide

By: Md. Rezaul Karim, Ajay Kumar N

Overview of this book

Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.
Table of Contents (9 chapters)

Generalized linear regression (GLR)

In an LR, the output is assumed to follow a Gaussian distribution. In contrast, in generalized linear models (GLMs), the response variable Yi follows some random distribution from a parametric set of probability distributions of a certain form. As we have seen in the previous example, following and creating a GLR estimator will not be difficult:

val glr = new GeneralizedLinearRegression()
.setFamily("gaussian")//continuous value prediction (or gamma)
.setLink("identity")//continuous value prediction (or inverse)

For the GLR-based prediction, the following response and identity link functions are supported based on data types (source: