  Explainable and interpretable unsupervised machine learning helps understand
the underlying structure of data. We introduce an ensemble analysis of machine
learning models to consolidate their interpretation. Its application shows that
restricted Boltzmann machines compress consistently into a few bits the
information stored in a sequence of five amino acids at the start or end of
$\alpha$-helices or $\beta$-sheets. The weights learned by the machines reveal
unexpected properties of the amino acids and the secondary structure of
proteins: (i) His and Thr have a negligible contribution to the amphiphilic
pattern of $\alpha$-helices; (ii) there is a class of $\alpha$-helices
particularly rich in Ala at their end; (iii) Pro occupies most often slots
otherwise occupied by polar or charged amino acids, and its presence at the
start of helices is relevant; (iv) Glu and especially Asp on one side, and Val,
Leu, Iso, and Phe on the other, display the strongest tendency to mark
amphiphilic patterns, i.e., extreme values of an "effective hydrophobicity",
though they are not the most powerful (non) hydrophobic amino acids.
