In this chapter, we will build on what we covered in Chapter 6, Building a Classification Model with Spark. While classification models deal with outcomes that represent discrete classes, regression models are concerned with target variables that can take any real value. The underlying principle is very similar--we wish to find a model that maps input features to predicted target variables. Like classification, regression is also a form of supervised learning.
Regression models can be used to predict just about any variable of interest. A few examples include the following:
- Predicting stock returns and other economic variables
- Predicting loss amounts for loan defaults (this can be combined with a classification model that predicts the probability of default, while the regression model predicts the amount in the case of a default)
- Recommendations (the Alternating Least Squares...
