Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems can machine learning solve, and how to talk about those problems with data scientists. Linear regression and feature selection are two such foundational topics.

Linear regression is a powerful technique for predicting numbers from other data. Imagine you have an imperative to predict basketball scores from game statistics, and you miraculously know absolutely nothing about basketball. The fact that a hoop is involved is news to you. You’ve found a dataset on stats.nba.com that has a bunch of statistics (free throws made, assists, blocks, three pointers), including the final score, and now you want to predict future scores given those stats.

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