Features vs. Variables

The predictors in a predictive model are sometimes given different terms by different disciplines. Traditional statisticians think in terms of variables. The machine learning community calls them features (also attributes or inputs). There is a subtle difference in meaning. In predictive modeling, depending on the nature of the data, considerable work may be required to transform and winnow down data and text to a usable set of predictors. This process is termed “feature engineering.” The comparable term in statistics – “variable selection” – does not imply such a wide ranging process.