[scikit-learn] Artificial neural network not learning lower values of the training sample

Hi All, I am trying to train an ANN but until now it is not learning the lower values of the training sample. I have tried using different python libraries to train ANN. The aim is to predict solar radiation from other weather parameters (regression problem). I think the ANN is confusing lower valu

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

mes wrote: > If the problem is that it’s confusing day and night, are you including > time of day as a parameter? > > Best wishes > Andrew > > @andrewholmes82 <http://twitter.com/andrewholmes82> > > > > > > > > > On 31 May 2016, at 16:55, muhamma

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

, 2016 at 10:47 AM, muhammad waseem < > m.waseem.ah...@gmail.com> wrote: > >> Thanks for your reply. I have day, month, hour, temp, relative humidity, >> Wind speed as my input variables. I can't think of any other dependant >> variables. It is quite strange to m

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

can compare to? It might be >>> helpful in seeing if this is a problem which can be learned. >>> >>> On Tue, May 31, 2016 at 10:47 AM, muhammad waseem < >>> m.waseem.ah...@gmail.com> wrote: >>> >>>> Thanks for your reply. I have day, mo

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

andrewholmes82> > > > > > > > > > On 31 May 2016, at 16:59, Andrew Holmes wrote: > > If the problem is that it’s confusing day and night, are you including > time of day as a parameter? > > Best wishes > Andrew > > @andrewholmes82 <http://twi

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

@andrewholmes82 <http://twitter.com/andrewholmes82> > > > > > > > > > On 31 May 2016, at 20:00, muhammad waseem > wrote: > > Yes, it has poor performance (higher errors) on lower values. > I have tried random forest but as I mentioned it did not give good re

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

e using the same data as them, could you > try to replicate their results first? > > Best wishes > Andrew > > @andrewholmes82 <http://twitter.com/andrewholmes82> > > > > > > > > > On 31 May 2016, at 20:05, muhammad waseem > wrote: > &

Re: [scikit-learn] Artificial neural network not learning lower values of the training sample

Sciences > Executive Editor, European Journal of Pure and Applied Mathematics > www.andrewhowe.com > http://www.linkedin.com/in/ahowe42 > https://www.researchgate.net/profile/John_Howe12/ > I live to learn, so I can learn to live. - me > <~~~~~~~> > > On

[scikit-learn] Random forest fitting very well

Hi All, I am trying to use random forests for a regression problem, with 10 input variables and one output variable. I am getting very good fit even with default parameters and low n_estimators. Even with n_estimator = 10, I get R^2 value of 0.95 on testing dataset (MSE=23) and a value of 0.99 for

Re: [scikit-learn] Random forest fitting very well

ed/sklearn.tree.export_graphviz.html#sklearn.tree.export_graphviz > [3] > http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html > > On 23 June 2016 at 10:20, muhammad waseem > wrote: > >> Hi All, >> I am trying to use random

Re: [scikit-learn] Random forest fitting very well

p using the powerful and complex models only when the data demands > it. > > On 23 June 2016 at 10:20, muhammad waseem > wrote: > >> Hi All, >> I am trying to use random forests for a regression problem, with 10 input >> variables and one output variable. I am

[scikit-learn] SVM number of support vectors

Hello All, I am trying to replicate the below figure and wanted to confirm that number of support vectors can be calculated by *support_vectors_* attribute in scikitlearn? [image: Inline image 1] Regards Waseem ___ scikit-learn mailing list scikit-learn

[scikit-learn] Extra trees tuning parameters

Hi All, I was wondering if you could please tell me what is the "nmin , the minimum sample size for splitting a node" (referred by Geurts et al., 2006) in scikit-learn API for Extra trees? Is it min_samples_split in skearn? Regards Waseem ___ scikit-lear