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SVM radial basis generate equation for hyperplane

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0

$begingroup$

I would be very grateful if I could receive some help regarding generating hyperplane equation. I need to generate an equation for hyperplane, I have two independent variables and one binary dependent variable.

Regarding this following equation for svm , f(x)=sgn( sum_i alpha_i K(sv_i,x) + b )

I have two independent variables (say P and Q) with 130 point values for each variable. I used svm radial basis function for binary classification (0 and 1) and I calculated for radial basis kernelized case,and now I have one column of 51 y (i) alpha (i) or (dual coeffficients), two columns of 51 sv (support vectors)for P and Q, and one single value for b . I received these using scikit SVC.

https://scikit-learn.org/stable/modules/svm.html

So, how can I generate the equation now? Can I multiply those 51 y (i) alpha (i) or (dual coeffficients) with 51 sv (support vectors) for each variable P and Q so that I have two coefficients for P and Q so that finally my equation appears as : f(x)=sgn( mP + nQ +b) where m = sum of the (product of 51 sv of P with 51 dual coefficients) and n = sum of the (product of 51 sv of Q with 51 dual coefficients).
i would be grateful for any kind of suggestion. Many thanks in advance.

machine-learning python scikit-learn svm machine-learning-model

share|improve this question

asked 1 hour ago

Alejandro Alejandro

1

New contributor

Alejandro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

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add a comment | 

0

$begingroup$

I would be very grateful if I could receive some help regarding generating hyperplane equation. I need to generate an equation for hyperplane, I have two independent variables and one binary dependent variable.

Regarding this following equation for svm , f(x)=sgn( sum_i alpha_i K(sv_i,x) + b )

I have two independent variables (say P and Q) with 130 point values for each variable. I used svm radial basis function for binary classification (0 and 1) and I calculated for radial basis kernelized case,and now I have one column of 51 y (i) alpha (i) or (dual coeffficients), two columns of 51 sv (support vectors)for P and Q, and one single value for b . I received these using scikit SVC.

https://scikit-learn.org/stable/modules/svm.html

So, how can I generate the equation now? Can I multiply those 51 y (i) alpha (i) or (dual coeffficients) with 51 sv (support vectors) for each variable P and Q so that I have two coefficients for P and Q so that finally my equation appears as : f(x)=sgn( mP + nQ +b) where m = sum of the (product of 51 sv of P with 51 dual coefficients) and n = sum of the (product of 51 sv of Q with 51 dual coefficients).
i would be grateful for any kind of suggestion. Many thanks in advance.

machine-learning python scikit-learn svm machine-learning-model

share|improve this question

asked 1 hour ago

Alejandro Alejandro

1

New contributor

Alejandro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

$begingroup$

I would be very grateful if I could receive some help regarding generating hyperplane equation. I need to generate an equation for hyperplane, I have two independent variables and one binary dependent variable.

Regarding this following equation for svm , f(x)=sgn( sum_i alpha_i K(sv_i,x) + b )

I have two independent variables (say P and Q) with 130 point values for each variable. I used svm radial basis function for binary classification (0 and 1) and I calculated for radial basis kernelized case,and now I have one column of 51 y (i) alpha (i) or (dual coeffficients), two columns of 51 sv (support vectors)for P and Q, and one single value for b . I received these using scikit SVC.

https://scikit-learn.org/stable/modules/svm.html

So, how can I generate the equation now? Can I multiply those 51 y (i) alpha (i) or (dual coeffficients) with 51 sv (support vectors) for each variable P and Q so that I have two coefficients for P and Q so that finally my equation appears as : f(x)=sgn( mP + nQ +b) where m = sum of the (product of 51 sv of P with 51 dual coefficients) and n = sum of the (product of 51 sv of Q with 51 dual coefficients).
i would be grateful for any kind of suggestion. Many thanks in advance.

machine-learning python scikit-learn svm machine-learning-model

share|improve this question

asked 1 hour ago

Alejandro Alejandro

1

New contributor

Alejandro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

share|improve this question

asked 1 hour ago

Alejandro Alejandro

1

New contributor

Alejandro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked 1 hour ago

Alejandro Alejandro

1

New contributor

Alejandro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 1 hour ago

Alejandro Alejandro

1

New contributor

Alejandro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 1 hour ago

Alejandro Alejandro

1