[scikit-learn] custom loss function

Andreas Mueller t3kcit at gmail.com
Thu Sep 14 16:41:42 EDT 2017

On 09/13/2017 05:31 PM, Thomas Evangelidis wrote:
> What about the SVM? I use an SVR at the end to combine multiple 
> MLPRegressor predictions using the rbf kernel (linear kernel is not 
> good for this problem). Can I also implement an SVR with rbf kernel in 
> Tensorflow using my own loss function? So far I found an example of an 
> SVC with linear kernel in Tensorflow and nothing in Keras. My 
> alternative option would be to train multiple SVRs and find through 
> cross validation the one that minimizes my custom loss function, but 
> as I said in a previous message, that would be a suboptimal solution 
> because in scikit-learn the SVR minimizes the default loss function.
Depends on what algorithm you want to use. As Frederico said, SVMs are 
usually solved as convex optimization problem on an infinite dimensional 
kernel space.
There is no straight-forward way to extend this to arbitrary losses afaik.
You can always make the kernel transformation explicit with Nystroem and 
solve a linear regression problem with custom loss on that.

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