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I am aware of both multi-class and multi-label classification.
But here is my use case, X1,X2,....Xn ==> Level1,Level2,Level3,Level4,Level5.. Image attached of a contrived example. enter image description here

So the model's different outputs are not independent of one another. So if Level1 is predicted as Fruits, level 2 can only be Green or Red.
One naive way i think is to concatenate all the labels together and create multiple new labels, like Fruits|Green|WaterMelon and Fruits|Red|Strawberry and Fruits|Red|Apple etc.The Label Powerset way.
However having 5 levels, this might have an explosion in the number of outputs to predict. Is there any elegant way to go about it ?

Thanks

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  • $\begingroup$ Why don't you just predict the leaves? If you need probabilities of the middle levels, you just need to add together the probabilities of the individual leaves. $\endgroup$
    – noe
    2 days ago

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