  Context. The determination of accurate photometric redshifts (photo-zs) in
large imaging galaxy surveys is key for cosmological studies. One of the most
common approaches are machine learning techniques. These methods require a
spectroscopic or reference sample to train the algorithms. Attention has to be
paid to the quality and properties of these samples since they are key factors
in the estimation of reliable photo-zs. Aims. The goal of this work is to
calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF
machine learning algorithm. Moreover, we want to develop techniques to assess
the incompleteness of the training sample and metrics to study how
incompleteness affects the quality of photometric redshifts. Finally, we are
interested in comparing the performance obtained with respect to the EAzY
template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have
emulated -- at brighter magnitude -- the training incompleteness with a
spectroscopic sample whose redshifts are known to have a measurable view of the
problem. We have used a principal component analysis to graphically assess
incompleteness and to relate it with the performance parameters provided by
DNF. Finally, we have applied the results about the incompleteness to the
photo-z computation on Y3 DES Deep Fields with DNF and estimated its
performance. Results. The photo-zs for the galaxies on DES Deep Fields have
been computed with the DNF algorithm and added to the Y3 DES Deep Fields
catalogue. They are available at
https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have
been developed to evaluate the performance in the absence of "true" redshift
and to assess completeness. We have studied... (Partial abstract)
