Testing

What is the primary goal of Support Vector Machines (SVM)?

• To maximize the margin between two classes

• To minimize the margin between two classes

• To calculate the distance between all data points

• To classify data points based on their color

What are the two predictors used in the example for classifying students?

• Letters of recommendation and GPA

• MCAT score and extracurricular activities

• Age and GPA

• GPA and MCAT score

What does a line represent in the context of SVM in two dimensions?

• A decision boundary

• A margin

• A support vector

• A data point

What is the term used for the observations that define the margin in SVM?

• Critical points

• Margin vectors

• Boundary points

• Support vectors

What is a disadvantage of SVM when dealing with outliers?

• Outliers are ignored by the SVM algorithm

• Outliers have no impact on the SVM algorithm

• Outliers can significantly affect the decision boundary

• Outliers increase the margin size

What is the difference between hard margin and soft margin SVM?

• Hard margin SVM cannot handle data that is not linearly separable, while soft margin SVM can.

• Soft margin SVM maximizes the margin, while hard margin SVM minimizes it.

• Soft margin SVM cannot handle outliers, while hard margin SVM can.

• Hard margin SVM is used for non-linear data, while soft margin SVM is not.

What does the decision boundary do in SVM?

• Minimizes the margin between classes

• Identifies the support vectors

• Calculates the distance between data points

• Separates data points into different classes

What is the margin in the context of SVM?

• The distance between the decision boundary and the nearest data point from either class

• The total number of support vectors

• The distance between the two furthest data points

• The average distance between all data points

How does SVM handle new observations?

• By changing the margin size for each new observation

• By ignoring new observations

• By recalculating the entire model for each new observation

• By predicting their class based on the decision boundary

What role do support vectors play in SVM?

• They are the only data points considered in the final model

• They are removed from the dataset before the model is trained

• They are used to calculate the distance between classes

• They define the margin and decision boundary