Say I have a classification problem that is multiclass and
characteristically hierarchical, e.g. 'edible', 'nutritious' and
'~nutritious' - so it can be represented like so
├── edible│ ├── nutritious│ └── ~nutritious└── ~edible
While one can get reasonable performance with classifiers that support
multiclass classification or using one-vs-one/all schemes for those that
don't, it may also be beneficial to separately train classifiers at each
level and concatenate them so the instances classified as 'edible' can be
classified as eithernutritious or not.
I would like to use scikit-learn estimators as building blocks and I am
wondering if I can make thePipeline support this or if I would need to
write my own estimator that implements the base estimator and possibly
BaseEnsemble to do this.
It has been mentioned before by @ogrisel on the mailing list
http://sourceforge.net/mailarchive/message.php?msg_id=31417048 and I'm
wondering if anyone has insights or suggestions on how to go about doing
this.
Thanks,
Louis
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