> On 12 Mar 2017, at 18:38, Gael Varoquaux <gael.varoqu...@normalesup.org> > wrote: > > You can use sample weights to go a bit in this direction. But in general, > the mathematical meaning of your intuitions will depend on the > classifier, so they will not be general ways of implementing them without > a lot of tinkering.
I see… to be honest for my purposes it would be enough to bypass the target binarization for the MLP classifier, so maybe I will just fork my own copy of that class for this. The purpose is two-fold, on the one hand use the probabilities generated by a very complex model (e.g. a massive ensemble) to train a simpler one that achieves comparable performance at a fraction of the cost. Any universal classifier will do (neural networks are the prime example). The second purpose it to use classes probabilities instead of observed classes at training time. In some problems this helps with model regularization (see section 6 of [1]) Cheers, J [1] https://arxiv.org/pdf/1503.02531v1.pdf <https://arxiv.org/pdf/1503.02531v1.pdf>
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