Implement sklearn suggestions for model maintainability

[kdoroschak]

The random forest classification option is being loaded using instructions here: https://scikit-learn.org/stable/modules/model_persistence.html

This will permanently require a specific version (or range of versions) of sklearn, and/or additional checks like they suggest (copied here):

In order to rebuild a similar model with future versions of scikit-learn, additional metadata should be saved along the pickled model:

The training data, e.g. a reference to an immutable snapshot

The python source code used to generate the model

The versions of scikit-learn and its dependencies

The cross validation score obtained on the training data

This should make it possible to check that the cross-validation score is in the same range as before.

Code/data location:

• classification/NanoporeTER_Random_Forest_classifier.ipynb.
  • If run on misl-a, it should run out of the box w/ no modifications.
• Training data is also here: https://github.com/uwmisl/NanoporeTER-data.

Training data is too large to include directly, but maybe we can create a small dataset for a unittest as a sentinel for "hey, something changed, check the sklearn version".