Interaction terms in logistic regression are features created by multiplying two or more existing features together.
They capture how the relationship between one predictor variable and the target depends on the value of another predictor variable.
They help model non-linear relationships that can't be captured by individual features alone, and can significantly improve model performance when features have interdependent effects on the outcome
Common examples of interactions include [age and income], [price and quantity], or [temperature and humidity].
Interaction terms must used carefully to avoid overfitting, and to maintain model interpretability
The decision to include interaction terms should be guided by your domain knowledge and statistical testing, as they increase model complexity and can make interpretation more challenging.
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