How do marginal effects be interpreted?

Marginal effects represent the change in the predicted probability of an outcome occurring as a result of a one-unit change in an independent variable, holding all other variables constant. In simpler terms, they quantify the impact of a specific predictor on the dependent variable. For example, in a logistic regression, a marginal effect of 0.05 for a variable means that increasing that variable by one unit increases the probability of the outcome by 5%. This interpretation helps in understanding the practical significance of each predictor in the model.