As a quick guess: the tool is attempting to perform a mathematical function on data that is not a number. Or rather, not a number that it understands as a “valid” number.
This was probably a value in your input. Less likely would be some value computed by the tool. Why? I would expect the tool itself to calculate values it also knows how to interpret.
This is the line in the code referenced. It is performing a check, and that looks like it is screening for whole numbers. I could be wrong, so double check.
→ galaxy-tools/qiime2__feature_classifier__fit_classifier_naive_bayes.xml at main · qiime2/galaxy-tools · GitHub
How I found that line of code: Tool form → Options menu at the top right → See in ToolShed → the TS repository links out to Development repository → then clicked into the xml file.
This tool doesn’t have test data directly in Github, but the TS repository also has a link out to the Content homepage which is usually the original tool authors repository. For this tool, it is here GitHub - qiime2/q2-feature-classifier: QIIME 2 plugin supporting taxonomic classification, which points to https://qiime2.org/.
A screenshot there references a Galaxy designed for just this tool, packaged into a container → q2Galaxy - Galaxy Community Hub. But the EU server also hosts it.
At the top of that page, see the link to Learn more at the top → Data files: QIIME 2 artifacts → https://docs.qiime2.org/2023.2/concepts/#data-files-qiime-2-artifacts where the .qza “artifact” data is described conceptually, not literally, so not helpful yet…
Linked at the bottom there are Tutorials → Tutorials — QIIME 2 2023.2.0 documentation
Maybe compare to the tutorial methods and example data? This probably includes how you uploaded the data to Galaxy (if the .qza wasn’t created after Upload), then compare/retrace your steps to find out where/how the odd value was introduced, fix that, and try a rerun.
Hope that helps. If you think you found an actual bug (example: tutorial data also fails), please follow up by submitting a bug report directly from the red error dataset and post back here with a shared history link (see Galaxy Training!) and we’ll help with confirming/reporting the technical issue to the developers, plus maybe can come up with some workaround.