Question - Random Forest Classification - NAN values output


I have a question regarding the random forest classifier. I would like to classify a raster stack of SPOT-6 (B01, B02, B03, B04, NDVI, NDWI, glcm_contrast, glcm_entropy, glcm_mean, glcm_correlation) with random forest:

raster → classification → supervised classification → random forest classifier

I used 500 trees and trained by vector shapefiles with 10 land use classes.

But some regions always get NaN values (e.g. a lake or arable land - see screenshot black regions) even though there are training data in these regions. I already deleted the confidence in the valid pixel expression.

Is there a reason why SNAP produces NaN values with the random forest classifier?

Thanks for any help and best regards!

Either it is a matter of low confidence (please see here: Landcover classification with Sentinel-1 GRD page 22-23) or one of your input datasets contains NaN values at these pixels (often happens with textures).