Does any H2O algorithm support multi-label classification?

Question

Is deep learning model supports multi-label classification problem or any other algorithms in H2O?

Orginal Response Variable -Tags:

apps, email, mail
finance,freelancers,contractors,zen99
genomes
gogovan
brazil,china,cloudflare
hauling,service,moving
ferguson,crowdfunding,beacon
cms,naytev
y,combinator
in,store,
conversion,logic,ad,attribution

After mapping them on the keys of the dictionary: Then

Response variable look like this:

[74]
[156, 89]
[153, 13, 133, 40]
[150]
[474, 277, 113]
[181, 117]
[15, 87, 8, 11]

Thanks

Answer 1

No, H2O only contains algorithms that learn to predict a single response variable at a time. You could turn each unique combination into a single class and train a multi-class model that way, or predict each class with a separate model.

Answer 2

Any algorithm that creates a model that gives you "finance,freelancers,contractors,zen99" for one set of inputs, and "cms,naytev" for another set of inputs is horribly over-fitted. You need to take a step back and think about what your actual question is.

But in lieu of that, here is one idea: train some word embeddings (or use some pre-trained ones) on your answer words. You could then average the vectors for each set of values, and hope this gives you a good numeric representation of the "topic". You then need to turn your, say, 100 dimensional averaged word vector into a single number (PCA comes to mind). And now you have a single number that you can give to a machine learning algorithm, and that it can predict.

You still have a problem: having predicted a number, how do you turn that number into a 100-dim vector, and from there in to a topic, and from there into topic words? Tricky, but maybe not impossible.

(As an aside, if you turn the above "single number" into a factor, and have the machine learning model do a categorization, to predicting the most similar topic to those it has seen before... you've basically gone full circle and will get a model identical to the one you started with that has too many classes.)