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I have read about SVM ...I know that it produces decision plane SVM does not explicitly produce a decision plane; it is not a parametric method. The decision plane implied by the fitted SVM can be visualized in two or three dimensions, but the plane merely results from the class labels and weights of the training observations. If the decision plane would ...


Remember, that in higly imbalanced data model does not learn anything as it minimises its objective function just by predicting everything to majority class. Yes Sample weight values you have assigned seems to do the right thing. What sample weight does is tweak the objective function to consider one error in predicting True class equivalent to 20 error in ...


Support Vector Machines are discriminative because they fit a hyperplane which separates two classes. So it learns a decision boundary which is the definition of discriminative methods.


You do not have to change your y labels, sklearn will do this for you.

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