What should we add?

Creating testing/training/validation datasets is a key step in machine learning workflows. Usually for Climate/Weather ML analysis, we split these datasets on a time dimension.

Scikit-learn has a function that does this for 2D arrays / pandas dataframes here. This function can't split xarray datasets.

Improvements on the scikit-learn implementation:

1. Built for xarray datasets
2. Can create a validation dataset (a third dataset) instead of doing it in two lines
3. Can split datasets up in a useful way for time series analysis (do not split up datasets randomly for time series analysis!)

Big questions:

1. Where should this go?
2. can we use Xr.dataset.parse_cf() in a smart way to pull the time dimension automagically? This might not be required anyways.

Reference

No response