  We develop taggers for multi-pronged jets that are simple functions of jet
substructure (so-called `subjettiness') variables. These taggers can be
approximately decorrelated from the jet mass in a quite simple way.
Specifically, we use a Logistic Regression Design (LoRD) which, even being one
of the simplest machine learning classifiers, shows a performance which
surpasses that of simple variables used by the ATLAS and CMS Collaborations and
is not far from more complex models based on neural networks. Contrary to the
latter, our method allows for an easy implementation of tagging tasks by
providing a simple and interpretable analytical formula with already optimised
parameters.
