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)

Linear regression

In this section, we will develop a predictive analytics model for predicting slowness in traffic for each row of the data using an LR algorithm. First, we create an LR estimator as follows:

val lr = new LinearRegression()

Then we invoke the fit() method to perform the training on the training set as follows:

println("Building ML regression model")
val lrModel =

Now we have the fitted model, which means it is now capable of making predictions. So, let's start evaluating the model on the training and validation sets and calculating the RMSE, MSE, MAE, R squared, and so on:

println("Evaluating the model on the test set and calculating the regression metrics")
// **********************************************************************
val trainPredictionsAndLabels...