Summary

Regression is the second of the two supervised learning methods in the Elastic Stack. The goal of regression is to take a trained dataset (a dataset that contains some features and a dependent variable that we want to predict) and distill it into a trained model. In regression, the dependent variable is a continuous value, which makes it distinct from classification, which handles discrete values. In this chapter, we have made use of the Elastic Stack's machine learning functionality to use regression to predict the sales price of a house based on a number of attributes, such as the house's location and the number of bedrooms. While there are numerous regression techniques available, the Elastic Stack uses gradient boosted decision trees to train a model.

In the next chapter, we will take a look at how supervised learning models can be used together with inference processors and ingest pipelines to create powerful, machine learning-powered data analysis pipelines...