Hail should support ordinal regression. Ordinal regression deals with a dependent variable whose values are finite, discrete and ordered. This differs from a categorical variable where each value can be represented by a binary variable. As I understand it, ordinal regression of a dependent variable
y is phrased in two steps: mapping from the independent variable
x to a threshold-space, then mapping from threshold-space to the dependent variable
y. For example:
The choices of \theta_i and w are fit according to some loss function. I do not fully understand the power of the intermediate threshold space. At the very least, it involves projecting the x vector onto a line which is then partitioned.