scikit-learn/scikit-learn

GMM covariance types examples overly complex / confusing

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#10,863 opened on Mar 23, 2018

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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

Research direction
Start by exploring the current example at the given URL. Understand how it uses supervised data and train/test split. Then, design a synthetic dataset using functions like `make blobs` to demonstrate the different covariance types. Consider adding a `get covariance` method to the GMM class as suggested. Look at the old GMM implementation for reference. Finally, simplify the example to focus on covariance types without classification.
Tech stack
python
Domain
machine learning
Issue type
Documentation
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2
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1-3 hours
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Clear
Prerequisites
Pythonscikit learnbasics of Gaussian Mixture Models
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65