For questions related to Feature Interactions, the interaction between two features is the change in the prediction that occurs by varying the features after considering the individual feature effects
From the book
Interpretable Machine Learning A Guide for Making Black Box Models Explainable by Christoph Molnar
Feature interactions are defined as
When features interact with each other in a prediction model, the prediction cannot be expressed as the sum of the feature effects, because the effect of one feature depends on the value of the other feature. Aristotle's predicate "The whole is greater than the sum of its parts" applies in the presence of interactions
It is often seen in machine learning experiments when two features combined through an arithmetic operation becomes more significant in explaining variances in the data, than the same two features separately. Creating a new feature through the interaction of existing features is known as feature interaction.
