Question: You are trying to classify data that is nonlinearly separable using SVM classifiers. There is a very large number of features for each instance. Under

You are trying to classify data that is nonlinearly separable using SVM classifiers. There is a very large number of features for each instance. Under these

circumstances it is best to:

Add polynomial features and then use the LinearSVC class.

Use the SVC class with a polynomial kernel.

Not use a SVM classifier since it cannot tackle this kind of job.

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