Similar to their use in regression, Generalized Additive Models (GAMs) can be applied to classification problems with qualitative responses. Assuming a binary response $Y$ that equals 0 or 1, we can extend the standard logistic regression model, $\log\left(\frac{p(X)}{1-p(X)}\right) = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_p X_p$, by replacing each linear component ($\beta_j X_j$) with a non-linear function ($f_j(X_j)$). The resulting GAM for classification is: $\log\left(\frac{p(X)}{1-p(X)}\right) = \beta_0 + f_1(X_1) + f_2(X_2) + \dots + f_p(X_p)$.