Modeling with Support Vector Machine in regression problem

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I have 20 independent variables(explanatory)and 1 dependent(respond) variable and I wish to use SVM as a regression problem to predict my dependent variable. but the point is that my independent(explanatory)variables are ranging differently.



e.g
x1 < 0 , x2 > 0 ,
{-1


The question is that:



Am I allow to use these explanatory variables in my SVM Regression model without normalizing them? or I should normal them first and then use them to estimate my respond variable?



If I should normal them, is there any specific normalization formula?




linear-algebra regression machine-learning estimation




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  • It depends... Do your independent variables (predictors) have the same unit (and you shouldn't rescale them)? Otherwise, SVM generally works better if your predictors have similar magnitude (in which case using a simple function like scale in R would work).
    – grixor
    Jul 18 at 16:36










  • actually, the predictors are from several sources with different units. Thanks for your response.
    – morteza
    Jul 18 at 16:46










  • Ah I see. Then it probably makes sense to rescale them.
    – grixor
    Jul 18 at 16:49














up vote
0
down vote

favorite












I have 20 independent variables(explanatory)and 1 dependent(respond) variable and I wish to use SVM as a regression problem to predict my dependent variable. but the point is that my independent(explanatory)variables are ranging differently.



e.g
x1 < 0 , x2 > 0 ,
{-1


The question is that:



Am I allow to use these explanatory variables in my SVM Regression model without normalizing them? or I should normal them first and then use them to estimate my respond variable?



If I should normal them, is there any specific normalization formula?




linear-algebra regression machine-learning estimation




share|cite|improve this question



















  • It depends... Do your independent variables (predictors) have the same unit (and you shouldn't rescale them)? Otherwise, SVM generally works better if your predictors have similar magnitude (in which case using a simple function like scale in R would work).
    – grixor
    Jul 18 at 16:36










  • actually, the predictors are from several sources with different units. Thanks for your response.
    – morteza
    Jul 18 at 16:46










  • Ah I see. Then it probably makes sense to rescale them.
    – grixor
    Jul 18 at 16:49












up vote
0
down vote

favorite









up vote
0
down vote

favorite











I have 20 independent variables(explanatory)and 1 dependent(respond) variable and I wish to use SVM as a regression problem to predict my dependent variable. but the point is that my independent(explanatory)variables are ranging differently.



e.g
x1 < 0 , x2 > 0 ,
{-1


The question is that:



Am I allow to use these explanatory variables in my SVM Regression model without normalizing them? or I should normal them first and then use them to estimate my respond variable?



If I should normal them, is there any specific normalization formula?




linear-algebra regression machine-learning estimation




share|cite|improve this question











I have 20 independent variables(explanatory)and 1 dependent(respond) variable and I wish to use SVM as a regression problem to predict my dependent variable. but the point is that my independent(explanatory)variables are ranging differently.



e.g
x1 < 0 , x2 > 0 ,
{-1


The question is that:



Am I allow to use these explanatory variables in my SVM Regression model without normalizing them? or I should normal them first and then use them to estimate my respond variable?



If I should normal them, is there any specific normalization formula?





linear-algebra regression machine-learning estimation





share|cite|improve this question










share|cite|improve this question




share|cite|improve this question









asked Jul 18 at 15:58









morteza

92




92











  • It depends... Do your independent variables (predictors) have the same unit (and you shouldn't rescale them)? Otherwise, SVM generally works better if your predictors have similar magnitude (in which case using a simple function like scale in R would work).
    – grixor
    Jul 18 at 16:36










  • actually, the predictors are from several sources with different units. Thanks for your response.
    – morteza
    Jul 18 at 16:46










  • Ah I see. Then it probably makes sense to rescale them.
    – grixor
    Jul 18 at 16:49
















  • It depends... Do your independent variables (predictors) have the same unit (and you shouldn't rescale them)? Otherwise, SVM generally works better if your predictors have similar magnitude (in which case using a simple function like scale in R would work).
    – grixor
    Jul 18 at 16:36










  • actually, the predictors are from several sources with different units. Thanks for your response.
    – morteza
    Jul 18 at 16:46










  • Ah I see. Then it probably makes sense to rescale them.
    – grixor
    Jul 18 at 16:49















It depends... Do your independent variables (predictors) have the same unit (and you shouldn't rescale them)? Otherwise, SVM generally works better if your predictors have similar magnitude (in which case using a simple function like scale in R would work).
– grixor
Jul 18 at 16:36




It depends... Do your independent variables (predictors) have the same unit (and you shouldn't rescale them)? Otherwise, SVM generally works better if your predictors have similar magnitude (in which case using a simple function like scale in R would work).
– grixor
Jul 18 at 16:36












actually, the predictors are from several sources with different units. Thanks for your response.
– morteza
Jul 18 at 16:46




actually, the predictors are from several sources with different units. Thanks for your response.
– morteza
Jul 18 at 16:46












Ah I see. Then it probably makes sense to rescale them.
– grixor
Jul 18 at 16:49




Ah I see. Then it probably makes sense to rescale them.
– grixor
Jul 18 at 16:49















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