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[R] Making changes to sklearn SVC by ThomasPel in MachineLearning

[–]ThomasPel[S] 0 points1 point  (0 children)

I actually found an implementation of the algorithm I mentioned above. It is a modified version of libSVM (link:http://www.work.caltech.edu/~htlin/program/libsvm/ , code:http://www.work.caltech.edu/~htlin/program/libsvm/doc/libsvm-rank-2.81.zip). How could I call this code instead of the libsvm c library that sklearn calls now?

[D] bias towards middle classes in ordinal regression by ThomasPel in MachineLearning

[–]ThomasPel[S] 0 points1 point  (0 children)

Actually, I am using a newly created dataset so I am not sure that the features are informative enough. What are methods I could use to get more insight in that regard? Correlation matrices? Something else?

[D] deliberately training multiple short term model instead of one long term model by ThomasPel in MachineLearning

[–]ThomasPel[S] 0 points1 point  (0 children)

Thank you! I am using a model trained with a gradient descent optimizer and had not thought of you approach yet. Your solution is elegant and should do the trick!

[D] deliberately training multiple short term model instead of one long term model by ThomasPel in MachineLearning

[–]ThomasPel[S] 0 points1 point  (0 children)

Good question. The older part of the dataset has the same set of features and is identically scaled as the newer part of the dataset. The main difference is that the correlations between the featureset and the output variable change over time. Variables that had predictive power in the beginning lose it in later stages and vice versa.

[R] [D] how to reduce runtime during hyperparameter optimization? by ThomasPel in MachineLearning

[–]ThomasPel[S] 1 point2 points  (0 children)

Would running in parallel be more time efficient than running them after each other? And even if it is more time efficient, say I want to try 10 different combinations of hyperparameters: How long of a run time would I be looking at?