feature cross

A synthetic feature formed by "crossing" categorical or bucketed features. For example, consider a "mood forecasting" model that represents temperature in one of the following four buckets: - `freezing` - `chilly` - `temperate` - `warm` And represents wind speed in one of the following three buckets: - `still` - `light` - `windy` Without feature crosses, the linear model trains independently on each of the preceding seven various buckets. So, the model trains on, for example,`freezing` independently of the training on, for example,`windy`. Alternatively, you could create a feature cross of temperature and wind speed. This synthetic feature would have the following 12 possible values: - `freezing-still` - `freezing-light` - `freezing-windy` - `chilly-still` - `chilly-light` - `chilly-windy` - `temperate-still` - `temperate-light` - `temperate-windy` - `warm-still` - `warm-light` - `warm-windy` Thanks to feature crosses, the model can learn mood differences between a`freezing-windy` day and a`freezing-still` day. If you create a synthetic feature from two features that each have a lot of different buckets, the resulting feature cross will have a huge number of possible combinations. For example, if one feature has 1,000 buckets and the other feature has 2,000 buckets, the resulting feature cross has 2,000,000 buckets. Formally, a cross is a Cartesian product. Feature crosses are mostly used with linear models and are rarely used with neural networks. See Categorical data: Feature crosses in Machine Learning Crash Course for more information.

Practitioners refer to feature cross when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.