What process involves combining features into a single feature, allowing a model to learn separate weights for each combination?

The process of combining features into a single feature, which allows a model to learn separate weights for each combination, is known as Feature Cross. Feature crossing is a technique used in feature engineering that enables the creation of new features from existing ones. By combining multiple features, the model can capture interactions and relationships that might otherwise be missed.

For example, if you have two binary features, “color” (red, blue) and “size” (small, large), creating a feature cross could generate features like “red_small,” “red_large,” “blue_small,” and “blue_large.” This allows the model to assign different weights to different combinations of these original features, which can lead to improved performance, especially in scenarios where interactions between features are significant.

In contrast, feature scaling standardizes or normalizes features, feature selection involves choosing a subset of relevant features, and feature transformation changes the representation of the data (e.g., through techniques such as logarithmic transformations or polynomial expansions), but does not specifically create combinations of features for interaction modeling. Hence, Feature Cross is the correct term that specifically refers to combining features to form new features that represent interactions between original features.