One vs one SVM for classes 1 and 5 but I have 10 classes in total. Should I train and test on all rows or...
$begingroup$
I want to use soft margin SVM for my dataset. My dataset contains 10 digits - 0 to 9.
I need to train using one-vs-one SVM classifier(one digit is class +1 and another digit is class -1) for digits '1'(+1) and '5'(-1).
Do I need to take subset from the training and testing dataset ?
Or should I test the SVM model on all the classes.
machine-learning python svm
asked 2 days ago
DukeLoverDukeLover
667
$endgroup$
add a comment |
$begingroup$
I want to use soft margin SVM for my dataset. My dataset contains 10 digits - 0 to 9.
I need to train using one-vs-one SVM classifier(one digit is class +1 and another digit is class -1) for digits '1'(+1) and '5'(-1).
Do I need to take subset from the training and testing dataset ?
Or should I test the SVM model on all the classes.
machine-learning python svm
asked 2 days ago
DukeLoverDukeLover
667
$endgroup$
add a comment |
$begingroup$
I want to use soft margin SVM for my dataset. My dataset contains 10 digits - 0 to 9.
I need to train using one-vs-one SVM classifier(one digit is class +1 and another digit is class -1) for digits '1'(+1) and '5'(-1).
Do I need to take subset from the training and testing dataset ?
Or should I test the SVM model on all the classes.
machine-learning python svm
asked 2 days ago
DukeLoverDukeLover
667
$endgroup$
I want to use soft margin SVM for my dataset. My dataset contains 10 digits - 0 to 9.
I need to train using one-vs-one SVM classifier(one digit is class +1 and another digit is class -1) for digits '1'(+1) and '5'(-1).
Do I need to take subset from the training and testing dataset ?
Or should I test the SVM model on all the classes.
machine-learning python svm
machine-learning python svm
asked 2 days ago
DukeLoverDukeLover
667
asked 2 days ago
DukeLoverDukeLover
667
edited 2 days ago
DukeLover
asked 2 days ago
DukeLoverDukeLover
667
asked 2 days ago
DukeLoverDukeLover
667
asked 2 days ago
DukeLoverDukeLover
667
667
add a comment |
add a comment |
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