A task in which a model must predict a numerical value for a specific scenario.
When you look up the price of a flight two weeks from today, a model is performing what’s called a “regression” task. For this user experience, the ML model must give you more than a discrete, yes/no type of response. To provide more nuanced information, the prediction is based on past data, in the form of continuous numerical values.
Imagine you have the task of designing a display of winter accessories in a store. Think of the task of classification as sorting winter accessories into neat bins, like sorting hats in one and scarves in another. This exercise would consider each accessory’s shape and other factors you and your store’s customers use to recognize what’s a scarf and what’s a hat. Think of a regression task as designing a complex window display with practical winter outerwear ensembles customized to your store’s snowy location. You want to include scarves and hats, as well as other items such as earmuffs, socks, fleece layers, and coats that you adjust based on the current weather, your past knowledge of what your customers need and want during this season, and the year’s fashion trends.
Regression predictions are used with a lot of versatility and inventiveness, powering incredibly complex user experiences that are capable of predicting changes in currency values, ranking songs to create a personalized playlist, or even determining image quality. When deciding if a regression model is appropriate for your users, a good place to start is the desired level of nuance and complexity in the final product or service. For a deeper example, learn how the Google Clips team used a regression model to build a hands-free camera.