Importance of using consistance qiime2 versions with classifiers

Thanks! How important is it to train the classifier with the same version of QIIME2 as we use for the rest of our analysis (assuming the version of Greengenes is the same)?
E.g. can I use the Greengenes 2022.10 classifier I trained using QIIME2 2023.2 for my next analysis using QIIME2 2023.5?


It can matter a lot or not at all. The changelogs (e.g., for 2023.5) will note any important user facing and behind the scenes changes between versions. If nothing is mentioned regarding q2-feature-classifier between the versions being considered, then it probably is fine.

It is important to note though that relying on the changelogs is not perfect. The libraries QIIME 2 depends on, like scikit-learn, also make new releases and it is not uncommon for the versions of dependencies to be updated with releases of QIIME 2. Relevant changes to dependencies can be harder to track, as there are a lot of dependencies, and may not be reflected directly in the QIIME 2 changelog.



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