  Large-scale surveys make huge amounts of photometric data available. Because
of the sheer amount of objects, spectral data cannot be obtained for all of
them. Therefore it is important to devise techniques for reliably estimating
physical properties of objects from photometric information alone. These
estimates are needed to automatically identify interesting objects worth a
follow-up investigation as well as to produce the required data for a
statistical analysis of the space covered by a survey. We argue that machine
learning techniques are suitable to compute these estimates accurately and
efficiently. This study promotes a feature selection algorithm, which selects
the most informative magnitudes and colours for a given task of estimating
physical quantities from photometric data alone. Using k nearest neighbours
regression, a well-known non-parametric machine learning method, we show that
using the found features significantly increases the accuracy of the
estimations compared to using standard features and standard methods. We
illustrate the usefulness of the approach by estimating specific star formation
rates (sSFRs) and redshifts (photo-z's) using only the broad-band photometry
from the Sloan Digital Sky Survey (SDSS). For estimating sSFRs, we demonstrate
that our method produces better estimates than traditional spectral energy
distribution (SED) fitting. For estimating photo-z's, we show that our method
produces more accurate photo-z's than the method employed by SDSS. The study
highlights the general importance of performing proper model selection to
improve the results of machine learning systems and how feature selection can
provide insights into the predictive relevance of particular input features.
