It finally works with nu=0.01 or less and the predictions are good. Is there a problem with that?
On 8 December 2016 at 12:57, Thomas Evangelidis <[email protected]> wrote: > > >> >> @Thomas >> I still think the optimization problem is not feasible due to your data. >> Have you tried balancing the dataset as I mentioned in your other >> question regarding the >> >> MLPClassifier? >> >> >> > Hi Piotr, > > I had tried all the balancing algorithms in the link that you stated, but > the only one that really offered some improvement was the SMOTE > over-sampling of positive observations. The original dataset contained 24 > positive and 1230 negative but after SMOTE I doubled the positive to 48. > Reduction of the negative observations led to poor predictions, at least > using random forests. I haven't tried it with > > MLPClassifier yet though. > > > > -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: [email protected] [email protected] website: https://sites.google.com/site/thomasevangelidishomepage/
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