A key ingredient for successful machine learning implementations (based on discussions with many data scientists, machine learning engineers, and product managers) is being able to map the business problem and the desired outcome to the appropriate machine learning problem (or having a frank conversation that machine learning will not solve the problem!). Classification and regression are two common machine learning techniques that are used.
In this chapter, we cover the basics of classification and regression and show you how to map a business use case to a classification or regression problem. You’ll learn how to use Microsoft Azure Machine Learning—specifically, automated ML—to automatically select the best classification or regression models for your specific use case.
Need to Get Started with Azure Machine Learning?
If you’re getting started with Azure Machine Learning, refer to Chapter 3 to understand the basic concepts before diving into this chapter.
In supervised learning, you have a set of independent features, X, and a target feature, Y. The machine learning task is to map from X → Y. Both classification and regression are supervised learning, with a requirement on the availability of labeled data.
To train a high-quality model that performs well for testing data and for generalizing new unseen data, examples need to be sufficiently representative of the test data. One underlying assumption ...