  The study of machine learning (ML) techniques for the autonomous
classification of astrophysical sources is of great interest, and we explore
its applications in the context of a multifrequency data-frame. We test the use
of supervised ML to classify blazars according to its synchrotron peak
frequency, either lower or higher than 10$^{15}$Hz. We select a sample with
4178 blazars labelled as 1279 high synchrotron peak (HSP: $\rm \nu$-peak >
10$^{15}$Hz) and 2899 low synchrotron peak (LSP: $\rm \nu$-peak < 10$^{15}$Hz).
A set of multifrequency features were defined to represent each source, that
includes spectral slopes ($\alpha_{\nu_1, \nu_2}$) between the radio,
infra-red, optical, and X-ray bands, also considering IR colours. We describe
the optimisation of five ML classification algorithms that classify blazars
into LSP or HSP: Random Forests (RF), Support Vector Machine (SVM), K-Nearest
Neighbours (KNN), Gaussian Naive Bayes (GNB) and the Ludwig auto-ML framework.
In our particular case, the SVM algorithm had the best performance, reaching
93% of balanced-accuracy. A joint-feature permutation test revealed that the
spectral slopes alpha-radio-IR and alpha-radio-optical are the most relevant
for the ML modelling, followed by the IR colours. This work shows that ML
algorithms can distinguish multifrequency spectral characteristics and handle
the classification of blazars into LSPs and HSPs. It is a hint for the
potential use of ML for the autonomous determination of broadband spectral
parameters (as the synchrotron $\nu$-peak), or even to search for new blazars
in all-sky databases.
