GMM covariance types examples overly complex / confusing · scikit-learn/scikit-learn#10863

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Documentationhelp wantedmodule:mixture

Description

http://scikit-learn.org/dev/auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py

I don't particularly like the example because it's supervised and uses a train/test split and identification with the original classes. I think it would be better to use a synthetic dataset and just show off the different covariance types.

It also fits the model on 4d data and only shows a 2d projection and that's not super intuitive imho.

Also, the example could be much simplified if we added a "get_covariance" function back to the model. I think we had that in the old GMM. Was there a reason not to add it to the new GMM? In many cases the user wants to be agnostic to the storage format of the covariance matrix, I think.

Contributor guide

Tech stack: python
Domain: machine learning
Issue type: feature
Difficulty: 3
Estimated time: half day
Activity status: stale
Clarity: clear
Prerequisites: Pythonscikit learn basics
Newbie friendliness: 65
Research direction: Review the current example at plot gmm covariances.py. Consider creating a simpler synthetic dataset instead of the supervised split. Look into adding a get covariance method to GMM if needed. See comments for possible oversight on the get covariance removal.