How do the summaries of Logistic Regression and SVM vary from each other?

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Anonymous · Accepted Answer

Both the approaches are linear classification approaches. First logistic
regression utilizes a sigmoid function to plot the data onto the sigmoid curve
and maximizes likelihood of the input. SVM, on the other hand, utilizes support
vectors or vectors which help create a margin which classifies the two classes
very well. This, along with kernel trick, the trick to add non-linearity to
modeling using a polynomial or rbf kernel without converting the data into the
space makes it more robust.t Both the models are binary classifiers and
relatively fast to train

How do the summaries of Logistic Regression and SVM vary from each other?

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