  The dipper is a novel class of young stellar object associated with large
drops in flux on the order of 10 to 50 per cent lasting for hours to days. Too
significant to arise from intrinsic stellar variability, these flux drops are
currently attributed to disk warps, accretion streams, and/or transiting
circumstellar dust. Dippers have been previously studied in young star forming
regions including the Orion Complex. Using Next Generation Transit Survey
(NGTS) data, we identified variable stars from their lightcurves. We then
applied a machine learning random forest classifier for the identification of
new dipper stars in Orion using previous variable classifications as a training
set. We discover 120 new dippers, of which 83 are known members of the Complex.
We also investigated the occurrence rate of disks in our targets, again using a
machine learning approach. We find that all dippers have disks, and most of
these are full disks. We use dipper periodicity and model-derived stellar
masses to identify the orbital distance to the inner disk edge for dipper
objects, confirming that dipper stars exhibit strongly extended sublimation
radii, adding weight to arguments that the inner disk edge is further out than
predicted by simple models. Finally, we determine a dipper fraction (the
fraction of stars with disks which are dippers) for known members of 27.8 plus
minus 2.9 per cent. Our findings represent the largest population of dippers
identified in a single cluster to date.
