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1.Which statement is NOT correct about SVM for a problem with 2 set of input features and a binary class of output?

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SVM is a good approach only for smaller datasets


SVM maximizes the margin between support vectors


Hyperplanes are decision boundaries that help classify the data points.


Support vectors are the features that are closer to the hyperplane



2.A scatter plot of two features for predicting three classes is shown below. What is the best machine learning model that we can fit on this information?

SVM with RBF kernel


Linear regression


Linear SVM


It is impossible to make any classification on this data even with low accuracy

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