Kernelized Ridge Regression implementation Error





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I am trying to implement kernelized ridge regression. There are 20000 data rows approximately and about 150 features.



This is the model being fit:




KernelRidge(alpha, kernelType, gamma=0.005, degree=3, coef0, kernel_params=None)


where kerneltype has been set to 'rbf' and 'linear', both times slowing down and eventually eating up a lot of memory



I dont think degree is causing the problem. I tried the same thing with degree=1 as well.



I also tried the same thing with gamma = 1 but faced the same issue.



Regular ridge, lasso and linear regression take not more than a second to complete (for the same data).



Where could I be going wrong?






scikit-learn







share|improve this question































    0















    I am trying to implement kernelized ridge regression. There are 20000 data rows approximately and about 150 features.



    This is the model being fit:




    KernelRidge(alpha, kernelType, gamma=0.005, degree=3, coef0, kernel_params=None)


    where kerneltype has been set to 'rbf' and 'linear', both times slowing down and eventually eating up a lot of memory



    I dont think degree is causing the problem. I tried the same thing with degree=1 as well.



    I also tried the same thing with gamma = 1 but faced the same issue.



    Regular ridge, lasso and linear regression take not more than a second to complete (for the same data).



    Where could I be going wrong?






    scikit-learn







    share|improve this question



























      0












      0








      0








      I am trying to implement kernelized ridge regression. There are 20000 data rows approximately and about 150 features.



      This is the model being fit:




      KernelRidge(alpha, kernelType, gamma=0.005, degree=3, coef0, kernel_params=None)


      where kerneltype has been set to 'rbf' and 'linear', both times slowing down and eventually eating up a lot of memory



      I dont think degree is causing the problem. I tried the same thing with degree=1 as well.



      I also tried the same thing with gamma = 1 but faced the same issue.



      Regular ridge, lasso and linear regression take not more than a second to complete (for the same data).



      Where could I be going wrong?






      scikit-learn







      share|improve this question
















      I am trying to implement kernelized ridge regression. There are 20000 data rows approximately and about 150 features.



      This is the model being fit:




      KernelRidge(alpha, kernelType, gamma=0.005, degree=3, coef0, kernel_params=None)


      where kerneltype has been set to 'rbf' and 'linear', both times slowing down and eventually eating up a lot of memory



      I dont think degree is causing the problem. I tried the same thing with degree=1 as well.



      I also tried the same thing with gamma = 1 but faced the same issue.



      Regular ridge, lasso and linear regression take not more than a second to complete (for the same data).



      Where could I be going wrong?






      scikit-learn




      scikit-learn






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 26 '18 at 14:45







      rahs

















      asked Nov 24 '18 at 17:09









      rahsrahs

      374214




      374214
























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