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It'd be good to clarify the boundaries of dask-glm and dask-ml. My motivation is building up a set of utilities in dask-ml for working generically with dask or NumPy arrays, and dask or pandas dataframes.
I'd like to move the estimator interface over to dask-ml
and leave all the optimization and regularization logic here. I think that families
would stay in dask_glm.
With that reorganization, the interface is:
- dask_glm for lower-level optimization routines on large dask arrays
- dask_ml for higher-level estimators, which can work on dask, numpy, or pandas data structures.
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