Machine Learning - Support Vector Machines (SVM) - MCQs
Machine Learning - Support Vector Machines (SVM) - MCQs 1. A Support Vector Machine can be used for A. Performing linear or nonlinear classification B. Performing regression C. For outlier detection D. All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A. Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called Soft margin classification Hard margin classification Strict margin classification Loose margin classification Ans: B 5. The main issues with hard margin classification are It only works if the data is linearly separable It is quite sensitive to outliers It is impossible to find a margin if the data is not l