  Recent work has leveraged the popular distributionally robust optimization
paradigm to combat overfitting in classical logistic regression. While the
resulting classification scheme displays a promising performance in numerical
experiments, it is inherently limited to numerical features. In this paper, we
show that distributionally robust logistic regression with mixed (i.e.,
numerical and categorical) features, despite amounting to an optimization
problem of exponential size, admits a polynomial-time solution scheme. We
subsequently develop a practically efficient column-and-constraint approach
that solves the problem as a sequence of polynomial-time solvable exponential
conic programs. Our model retains many of the desirable theoretical features of
previous works, but -- in contrast to the literature -- it does not admit an
equivalent representation as a regularized logistic regression, that is, it
represents a genuinely novel variant of logistic regression. We show that our
method outperforms both the unregularized and the regularized logistic
regression on categorical as well as mixed-feature benchmark instances.
