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)

Scala for Tree-Based Ensemble Techniques

In the previous chapter, we solved both classification and regression problems using linear models. We also used logistic regression, support vector machine, and Naive Bayes. However, in both cases, we haven't experienced good accuracy because our models showed low confidence.

On the other hand, tree-based and tree ensemble classifiers are really useful, robust, and widely used for both classification and regression tasks. This chapter will provide a quick glimpse at developing these classifiers and regressors using tree-based and ensemble techniques, such as decision trees (DTs), random forests (RF), and gradient boosted trees (GBT), for both classification and regression. More specifically, we will revisit and solve both the regression (from Chapter 2, Scala for Regression Analysis) and classification (from Chapter 3, Scala for Learning Classification) problems we discussed previously.

The following topics will be covered in this chapter:

  • Decision trees and tree ensembles
  • Decision trees for supervised learning
  • Gradient boosted trees for supervised learning
  • Random forest for supervised learning
  • What's next?