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FAQ:
Q: Why do we need W vector in SVM?
A: I need the w vector to check the weights given to each of the features and see which ones are the most valuable. Show activity on this post. coef_ are the weights assigned to the features; (Note it only works for linear SVM) support_vectors_ and support_ are the support vectors and the corresponding index;
Q: How to find higher weights using wighted SVM in machine learning classification?
A: how to find higher weights using wighted SVM in machine learning classification. plz suggest.. It depends if you talk about the linearly separable or non-linearly separable case. In the former, the weight vector can be explicitly retrieved and represents the separating hyper-plane between the two classes.
Q: What are dual weights in SVM?
A: I’ll assume that you are referring to dual weights (typically denoted by the vector alpha). All predictions for SVM models — and more generally models resulting from kernel methods — can be expressed as a linear combination of kernel evaluations between (some) training instances (the support vectors) and the test instance.
Q: What is the best way to express SVM model predictions?
A: All predictions for SVM models — and more generally models resulting from kernel methods — can be expressed as a linear combination of kernel evaluations between (some) training instances (the support vectors) and the test instance. This follows from the so-called representer theorem (cfr. the link).