In the previous chapter, we got familiar with supervised and unsupervised learning. Another standard taxonomy of the machine learning methods is based on the label is from continuous or discrete space. Even if the discrete labels are ordered, there is a significant difference, particularly how the goodness of fit metrics is evaluated.
In this chapter, we will cover the following topics:
Learning about the origin of the word regression
Learning metrics for evaluating the goodness of fit in continuous and discrete space
Discussing how to write simple code in Scala for linear and logistic regression
Learning about advanced concepts such as regularization, multiclass predictions, and heteroscedasticity
Discussing an example of MLlib application for regression tree analysis
Learning about the different ways of evaluating classification models