  We demonstrate the application of a two stage machine learning algorithm that
enables to correlate the electrical signals from a GaAs$_x$N$_{1-x}$ circular
polarimeter with the intensity, degree of circular polarization and handedness
of an incident light beam. Specifically, we employ a multimodal logistic
regression to discriminate the handedness of light and a 6-layer neural network
to establish the relationship between the input voltages, the intensity and
degree of circular polarization. We have developed a particular neural network
training strategy that substantially improves the accuracy of the device. The
algorithm was trained and tested on theoretically generated photoconductivity
and on photoluminescence experimental results. Even for a small training
experimental dataset (70 instances), it is shown that the proposed algorithm
correctly predicts linear, right and left circularly polarized light
misclassifying less than $1.5\%$ of the cases and attains an accuracy larger
than $97\%$ in the vast majority of the predictions ($92\%$) for intensity and
degree of circular polarization. These numbers are significantly improved for
the larger theoretically generated datasets (4851 instances). The algorithm is
versatile enough that it can be easily adjusted to other device configurations
where a map needs to be established between the input parameters and the device
response. Training and testing data files as well as the algorithm are provided
as supplementary material.
