Gigaom How Machines Learn: The Top Four Approaches to ML in Business

Supervised learning outputs typically have one of two forms. Regression outputs are real-valued numbers that exist in a continuous space. For instance, many of Vidora's eCommerce customers want to forecast how much money each customer is likely to spend, so that high-value customer may be targeted with personalized promotional offers. A simple linear regression structures this problem through the familiar formula y mx b, where y is predicted expenditure and x is some attribute of each customer -- say, number of site visits. During training, we supply labeled input-output pairs -- i.e. customers for which transaction history is already known -- and the algorithm finds the optimal parameters m and b to make this relationship as accurate as possible.