Monotonicity constraints are used in machine learning and statistical models to ensure that the relationship between features and the target variable is either entirely non-decreasing or non-increasing. This constraint is particularly useful in scenarios where domain knowledge dictates that an increase or decrease in a feature should consistently lead to an increase or decrease in the prediction, improving model interpretability and trustworthiness.
