  Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV
longitudinally polarized electrons off the proton has been measured using the
CLAS12 detector at Jefferson Lab. Two separate channels, $\pi^+\pi^0$ and
$\pi^-\pi^0$, were analyzed, requiring the reconstruction of diphoton pairs. In
this analysis, we addressed the problem of false neutral particles being
reconstructed by CLAS12's event builder, polluting the otherwise physical
combinatorial background underneath the $\pi^0$ peak. A photon classifier using
a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo
simulations to reduce the amount of background $\pi^0$'s. We show that the
nearest-neighbor features learned by the model lead to a substantial increase
in signal vs. background discrimination compared to previous CLAS12 $\pi^0$
analyses. The machine learning approach recovers several times more dihadron
statistics for the dataset.
