Re: [scikit-learn] meta-estimator for multiple MLPRegressor

Stuart, I didn't see LASSO performing well, especially with the second type of data. The alpha parameter probably needs adjustment with LassoCV. I don't know if you have read my previous messages on this thread, so I quote again my setting for MLPRegressor.

Re: [scikit-learn] meta-estimator for multiple MLPRegressor

Thomas, Jacob's point is important -- its not the number of features that's important, its the number of free parameters. As the number of free parameters increases, the space of representable functions grows to the point where the cost function is minimized by having a single parameter explain

Re: [scikit-learn] Specify boosting percentage using Randomoversampling?

Well actually, i'm able to answer this myself. Its the ratio attribute (see: http://contrib.scikit-learn.org/imbalanced-learn/generated/imblearn.over_sampling.RandomOverSampler.html ) :) :) On Tue, Jan 10, 2017 at 12:36 PM, Suranga Kasthurirathne < suranga...@gmail.com> wrote: > > Hi all, > >

Re: [scikit-learn] Specify boosting percentage using Randomoversampling?

Is maybe this contrib what you are looking for? Take a close look to see whether it does what you expect. http://contrib.scikit-learn.org/imbalanced-learn/auto_examples/over-sampling/plot_smote.html On Tue, Jan 10, 2017 at 6:36 PM, Suranga Kasthurirathne < suranga...@gmail.com> wrote: > > Hi

[scikit-learn] Specify boosting percentage using Randomoversampling?

Hi all, I apologize - i've been looking for this answer all over the internet, and it could be that I'm not googling the right terms. For managing unbalanced datasets, Weka has SMOTE, and scikit has randomoversampling. In weka, we can ask it to boost by a given percentage (say 100%) so an

Re: [scikit-learn] Generalized Discriminant Analysis with Kernel

Thank you very much for your info on Nystroem kernel approximator. I appreciate it! Best, Raga On Tue, Jan 10, 2017 at 7:47 AM, wrote: > Send scikit-learn mailing list submissions to > scikit-learn@python.org > > To subscribe or unsubscribe via the