A feature cross is a synthetic feature created by combining two or more categorical or bucketed features. It allows a model, especially a linear model, to learn interactions between feature values that would not be captured by treating them independently.
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: - - - - And represents wind speed in one of the following three buckets: - - - Without feature crosses, the linear model trains independently on each of the preceding seven various buckets. So, the model trains on, for example, independently of the training on, for example, . Alternatively, you could create a feature cross of temperature and wind speed. This synthetic feature would have the following 12 possible values: - - - - - - - - - - - - Thanks to feature crosses, the model can learn mood differences between a day and a 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.