Which of the following is a disadvantage of SVM?Answer areaIt works well with high-dimensional dataIt is sensitive to the choice of kernel and regularization parameterIt can handle both linear and non-linear dataIt has a clear geometric interpretation

Question 4In which cases would we want to consider using SVM?

What kernel can be used in SVM to handle non-linear data?Answer areaLinear KernelPolynomial KernelRadial Basis Function (RBF) KernelAll of the above

4.Question 4In which cases would we want to consider using SVM?1 pointWhen we want multiple decision boundaries with varying weights.When we desire probability estimates for each class.When we desire efficiency when using large datasets.When mapping the data to a higher dimensional feature space can better separate classes.

Which of the following is a disadvantage of clustering?Answer areaIt can handle only numerical dataThe results can be highly sensitive to the choice of distance metricIt requires a lot of labeled training dataIt is always computationally expensive

Which of the following is a disadvantage of KNN?Answer areaIt is easy to implementIt can handle multi-class classificationIt is computationally expensive for large datasetsIt performs well with a small amount of data