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The problem occurs when doing OOS validation and multiprocessing by the looks. A .model file is created and looks fine when I check it... as in, I loaded one created and it had right number of feature importances, not tried prediction. Same process/data worked on out of sample when not run multiprocessing. Model exists when checked in predict.py - kwargs and RandomForestTransformed get returned ok - and that branch clearly works fine in general - I have run quite a lot of multiprocessing regressions with no OOS section with no problem.
The problem occurs when doing OOS validation and multiprocessing by the looks. A .model file is created and looks fine when I check it... as in, I loaded one created and it had right number of feature importances, not tried prediction. Same process/data worked on out of sample when not run multiprocessing. Model exists when checked in predict.py - kwargs and RandomForestTransformed get returned ok - and that branch clearly works fine in general - I have run quite a lot of multiprocessing regressions with no OOS section with no problem.
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