Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree)
Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree) --------------------------------------------------------------------- 1. A Support Vector Machine can be used for Performing linear or nonlinear classification Performing regression For outlier detection All of the above Ans: 4 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? True False Ans: 1 3. Support Vector Machines are not sensitive to feature scaling True False Ans: 2 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: 2 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 linearly separable All of the above A