Hi @Angela and @colinbrislawn ,
This is not a bug, this is most likely due to mixed read orientations; trimming to different lengths is probably changing the inclusion or order of sequences that are used for read orientation prediction. See here for an explanation:
Some of your reads are being poorly classified no matter the direction, so it could also be that there are many non-target reads in your samples that are interfering with the orientation detector (since the orientation is chosen based on match to a reference sequence).
You can also specify the read orientation if this is known and you do not want classify-sklearn to choose for you.