Scala for Machine Learning

Summary

This concludes the description and implementation of the linear and logistic regression and the concept of regularization to reduce overfitting. Your first analytical projects using machine learning will (or did) likely involve a regression model of some type. Regression models, along with the Naïve Bayes classification, are the most understood techniques for those without a deep knowledge of statistics or machine learning.

After the completion of this chapter, you will hopefully have a grasp on the following topics:

• The concept of linear and nonlinear least squares-based optimization
• The implementation of ordinary least square regression as well as logistic regression
• The impact of regularization with an implementation of the ridge regression ...