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I have a ML problem with 300 variables to predict, Meaning I have a multi-label binary classification problem with a Y space of 300 and with about 2000 rows only. Thus we assume that there are 2^300 permutations. However, it is not so.

Let me explain with an example.
X1,....X400 | Y1,Y2,Y3...........Y299,Y300
'healthy',..'sad'| 0,1..1,0
'unhealthy'...'sad'|1,0,....0

However when i aggregate across the rows,there are maybe about 100 combinations far less than 2^300 combinations such as this. so Y1,Y2,Y100 => C1 OR Y33,Y44,Y291,Y299,Y300 =>C2 ... till C100
Thus, I can change it to a multi-class problem to predict these 100 classes instead.
However, there are a lot of classes with count of 1 or 2. More than 50%. Thus, this approach doesnt work so well.

Is there an algorithmic way to still do the multi-label but by grouping the variables in the most optimal way like from Y space to Z space with dimension 5 for example.
{Y1,Y10,Y222,Y232}=> Z1 can be grouped together
{Y2...} => Z2 can be grouped together
Thus,we can predict on the Z space instead. At this point, I am not sure whether the Y variables need to be exclusive to one Z group or not.

But I am open to all suggestions at this moment ..

Cheers

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You may find this resource helpful : https://xang1234.github.io/multi-label/

One possibility is to cluster similar labels together so that they are processed together by the multilabel classification algorithms. Community detection methods such as the Louvain algorithm allow us to cluster the label graph. This is implemented in the NetworkXLabelGraphClusterer with the parameter methods = louvain.

A resource for splitting your data into (hopefully) balanced train,val,test sets (not so easy because you are in a multi label setting) : http://scikit.ml/stratification.html

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  • $\begingroup$ Thanks for the idea. I have tried the graph algo to reduce it to 10 Y dimensions. The metrics are better now. However, have to run through with the stakeholders if this is Ok with them. $\endgroup$
    – prog_guy
    Sep 17 '20 at 9:57

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