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We investigate properties of a classifier applied to the measurements of theCP state of the Higgs boson in $H\rightarrow\tau\tau$ decays. The problem isframed as binary classifier applied to individual instances. Then the priorknowledge that the instances belong to the same class is used to define themulti-instance classifier. Its final score is calculated as multiplication ofsingle instance scores for a given series of instances. In the paper we discussproperties of such classifier, notably its dependence on the number ofinstances in the series. This classifier exhibits very strong random dependenceon the number of epochs used for training and requires careful tuning of theclassification threshold. We derive formula for this optimal threshold.

A multi-instance deep neural network classifier: application to Higgs boson CP measurement