Setting best SVM hyper parameters2019 Community Moderator ElectionTune hyperparameters for cost-sensitive classificationDoes importance of SVM parameters vary for subsample of data?$chi^2$ kernel SVM performance issuehow to explain the behaviour: linear svm does better than non-linear RBFUsing GridSearchCV for custom kernel SVM in scikit-learnNyström approximation of the non-linear mapping $phi$ for a RBF kernel - what is the impact of weak duality?Poor performance of SVM after training for rare eventsIs splitting the data set into train and validation applicable in unsupervised learning?Improve precision of binary classification - SVM in MatlabHow to set hyperparameters in SVM classification

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Setting best SVM hyper parameters



2019 Community Moderator ElectionTune hyperparameters for cost-sensitive classificationDoes importance of SVM parameters vary for subsample of data?$chi^2$ kernel SVM performance issuehow to explain the behaviour: linear svm does better than non-linear RBFUsing GridSearchCV for custom kernel SVM in scikit-learnNyström approximation of the non-linear mapping $phi$ for a RBF kernel - what is the impact of weak duality?Poor performance of SVM after training for rare eventsIs splitting the data set into train and validation applicable in unsupervised learning?Improve precision of binary classification - SVM in MatlabHow to set hyperparameters in SVM classification










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$begingroup$


I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?










share|improve this question











$endgroup$
















    2












    $begingroup$


    I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?










    share|improve this question











    $endgroup$














      2












      2








      2





      $begingroup$


      I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?










      share|improve this question











      $endgroup$




      I have a non linear data set, and I am using SVM (RBF kernel) to build a classification model, but not sure how to set the best hyperparameters of the SVM, C and gamma in Matlab fitcsvm. And is it acceptable if it is trial and error approach setting arbitrary values, until finding the best performance?







      classification svm matlab hyperparameter hyperparameter-tuning






      share|improve this question















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      share|improve this question




      share|improve this question








      edited Mar 26 at 6:51







      gin

















      asked Mar 26 at 6:45









      gingin

      1667




      1667




















          2 Answers
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          active

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          1












          $begingroup$

          Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:




          1. Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.


          2. Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)

          Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.

          Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.






          share|improve this answer









          $endgroup$




















            1












            $begingroup$

            Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)



            You can use various techniques for that, for example:



            • Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.

            That can help you find an average solution fast, but remember that this comes without warranty.



            • Exhaustive Grid Search


            • Randomized Parameter Optimization


            • Genetic Algorithms


            Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.



            Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)






            share|improve this answer











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              2 Answers
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              2 Answers
              2






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              1












              $begingroup$

              Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:




              1. Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.


              2. Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)

              Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.

              Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.






              share|improve this answer









              $endgroup$

















                1












                $begingroup$

                Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:




                1. Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.


                2. Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)

                Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.

                Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.






                share|improve this answer









                $endgroup$















                  1












                  1








                  1





                  $begingroup$

                  Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:




                  1. Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.


                  2. Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)

                  Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.

                  Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.






                  share|improve this answer









                  $endgroup$



                  Sadly, there is no easy solution for hyperparameter tuning. Basically, you have two options:




                  1. Manual Adjustment: Read the documentation of the SVM and dive into the corresponding literature. Try to understand how the different kernel functions work and which functions are appropriate for your classification problem. Set the hyperparameters to the best of your knowledge and then observe what difference slight deviations make. Of course, there will still be some degree of trial and error in this approach.


                  2. Grid Search: For all categoric parameters (e.g. kernel function), set up a list of all possible categories and for all metric parameters (e.g. epsilon), define a range of reasonable values. This is your hyperparameter space. Subsequently, run a multitude of SVMs with randomly selected parameters from the hyperparameter space. In the end, you choose the hyperparameter combination with the highest accuracy. (Note: there are also other approaches with more sophisticated methods than random selection)

                  Both methods are widely used, however, if you are not very familiar with SVM's, I highly recommend the manual adjustment.

                  Of course, it is very tempting to simply set up a grid search, grab something to eat and later pick the best performing hyperparameter combination. But in the end, you will not learn anything and you will not know if these hyperparameters are a reasonable choice for your problem. Broadening your knowledge will be beneficial in the future, both because it reduces the effort for future manual adjustments and because in the future you will be able to narrow down the hyperparameter space for the grid search, which will significantly reduce computing time.







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Mar 26 at 11:24









                  georg_ungeorg_un

                  586




                  586





















                      1












                      $begingroup$

                      Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)



                      You can use various techniques for that, for example:



                      • Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.

                      That can help you find an average solution fast, but remember that this comes without warranty.



                      • Exhaustive Grid Search


                      • Randomized Parameter Optimization


                      • Genetic Algorithms


                      Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.



                      Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)






                      share|improve this answer











                      $endgroup$

















                        1












                        $begingroup$

                        Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)



                        You can use various techniques for that, for example:



                        • Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.

                        That can help you find an average solution fast, but remember that this comes without warranty.



                        • Exhaustive Grid Search


                        • Randomized Parameter Optimization


                        • Genetic Algorithms


                        Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.



                        Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)






                        share|improve this answer











                        $endgroup$















                          1












                          1








                          1





                          $begingroup$

                          Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)



                          You can use various techniques for that, for example:



                          • Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.

                          That can help you find an average solution fast, but remember that this comes without warranty.



                          • Exhaustive Grid Search


                          • Randomized Parameter Optimization


                          • Genetic Algorithms


                          Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.



                          Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)






                          share|improve this answer











                          $endgroup$



                          Well, there is a bunch of articles that tries to tackle this problem but basically, to guarantee a good solution you will need to do Grid Search (sklearn tutorial on it)



                          You can use various techniques for that, for example:



                          • Binary Gridsearch: Try the maximum and the minimum value in the interval you want, and the middle point, check which side is best and set the minimum or the maximum as your previous middle point according.

                          That can help you find an average solution fast, but remember that this comes without warranty.



                          • Exhaustive Grid Search


                          • Randomized Parameter Optimization


                          • Genetic Algorithms


                          Check the link for sklearn for more information. I research on kernel methods and this is aways a bit annoying thing to tackle.



                          Note: If you're only going to try RBF I would advise to use $sigma$ as a value betwen the norm of your training samples $pm 80%$. You could also normalize you vectors to norm 1 and limit the search to $0.2$ to $1.8$. Usually too small $sigma$ gives high sensibility to noise while too big aproximates a straight line (loses non-linear power)







                          share|improve this answer














                          share|improve this answer



                          share|improve this answer








                          edited Mar 26 at 13:21

























                          answered Mar 26 at 11:22









                          Pedro Henrique MonfortePedro Henrique Monforte

                          1157




                          1157



























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