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So the command can compute the features. Does it close this post?

Cloud you provide your parameter file and your data, or at least a sample of it? I never saw such an error. If you have strategies to try, I would say that you should try them to gain some experience. In terms of classification, practice is a good thing. Begin with a small subset of your data before...

The message is (almost) explicit, "scaled point features need a STAT". Only one line is wrong in your parameter file : feature: Z_SC0.5_PC1 should read (replace STAT by any tag among MEAN MODE MEDIAN STD RANGE SKEW) feature: Z_SC0.5_STAT_PC1 as specified in https://lidar.univ-rennes.fr/sit...

"feature: DZ10_SC0_corepoints_CTX_2" is an old version of the syntax (still valid but deprecated because confusing as features are always computed at the core point). You should better write "feature: DZ10_SC0_CTX_2". The reference document for the syntax is here: https://lidar.u...

By configuration I meant 3DMASC settings. Yes, being able to reproduce the issue is mandatory.

I launched a training with the same parameter file and the same Random Trees dialog parameters. The classifier is quite large, about 700Mo, but not as much as yours. I can load it after to classify other points. Are you sure about your configuration?

Please provide the initial parameter file.
Did you launch the training on the overall dataset at once?

Your yaml seems quite large.
What were your parameters in the training Dialog?
max depth
max tree count
active var count
min sample count

Please provide the classifier.yaml file for testing.
Which laz did you use among the SNCF dataset? There are 16 files available.

The code of the plugin has been updated. VERT should be available as a feature in a future release of CloudCompare.