A monotonic constraint tells a model that increasing a feature should never make the prediction move in the wrong direction. The relationship can still be nonlinear, but its direction is controlled.
This is useful when domain knowledge is strong. For example, if a product becomes more expensive while everything else stays fixed, a pricing model should not learn that customers become more likely to accept it.
