Improve missing-data completion methods using data imputation

Aifred-Health / VulcanAI

A high level deep learning framework for quickly prototyping networks with added tools in data visualisation, model interpretability and performance metrics

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Improve missing-data completion methods using data imputation #94

Open rfratila opened 5 years ago

rfratila commented 5 years ago

Potential things to implement:

New functionality from scikit-learn: https://github.com/scikit-learn/scikit-learn/pull/11977
https://dev.acquia.com/blog/how-to-handle-missing-data-in-machine-learning-5-techniques/09/07/2018/19651

nirtiac commented 5 years ago

@mrkrause have you made progress on this for your own work? Is this something that you could integrate?

nirtiac commented 5 years ago

Will be added once https://scikit-learn.org/dev/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer has been merged into the stable branch of sklearn.