Ensemble Methods for Machine Learning

7 Learning with continuous and count labels

This chapter covers

• Regression in machine learning
• Loss and likelihood functions for regression
• When to use different loss and likelihood functions
• Adapting parallel and sequential ensembles for regression problems
• Using ensembles for regression in practical settings

Many real-world modeling, prediction, and forecasting problems are best framed and solved as regression problems. Regression has a rich history predating the advent of machine learning and has long been a part of the standard statistician’s toolkit.

Regression techniques have been developed and widely applied in many areas. Here are just a few examples:

• Weather forecasting—To predict the precipitation tomorrow using data from today, ...