[Mistakenly posted as separate thread before, please ignore previous post]
I see. Thank you for the advice. This was initial novice iteration of the
solution and needs improvement of course. In terms of which, in order to
keep the behaviour of the classifier consistent, instead of a single
classifier with thousands of categories wouldn't it be better
to build an ensemble of classifiers with a hierarchy(similar dataset, less
collision since the noise from the other classes is reduced and
I can define the solution-flow custom to the category chosen) say,
Classifiers
|
----------------------------------------
| | ------- |
Type A Type B Type N (total types = 5 or 6)
|
| --
-----------------------------------
| | -------- |
Category A Category B Category N (N = atmost 1000)
Is there a way I can achieve this with scikit [i.e. unless I am mistaken
for a sample I need to also negatively train on the other classifiers]
I do appreciate clearing up the holes in my understanding.
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