  We study unbinned multivariate analysis techniques, based on Statistical
Learning, for indirect new physics searches at the LHC in the Effective Field
Theory framework. We focus in particular on high-energy $ZW$ production with
fully leptonic decays, modeled at different degrees of refinement up to NLO in
QCD. We show that a considerable gain in sensitivity is possible compared with
current projections based on binned analyses. As expected, the gain is
particularly significant for those operators that display a complex pattern of
interference with the Standard Model amplitude. The most effective method is
found to be the "Quadratic Classifier" approach, an improvement of the standard
Statistical Learning classifier where the quadratic dependence of the
differential cross section on the EFT Wilson coefficients is built-in and
incorporated in the loss function. We argue that the Quadratic Classifier
performances are nearly statistically optimal, based on a rigorous notion of
optimality that we can establish for an approximate analytic description of the
$ZW$ process.
