Question

1 (10 points) Multiple Choice 1. Which of the following statement(s) is/are true regarding the SVM classifier? A. The margin definition in the SVM formulation can be considered as a regu- larization term to prevent overfitting. B. Any function can be used a kernel function. C. Using a valid kernel, an SVM classifier can be trained without knowing the feature values for each sample. D. The so-called ""support vectors"" refer to the positive and negative planes. 2. Which of the following statement(s) is/are true regarding the decision tree classifier? A. When training a decision tree classifier, the depth of the tree goes linearly with respect to the number of training samples. B. The training objective function for a decision tree classifier has in general no analytic form to optimize for. C. Tree pruning can be used to prevent overfitting. D. In general, the deeper a decision tree is, the more complex the decision boundary is.

          1 (10 points) Multiple Choice
1. Which of the following statement(s) is/are true regarding the SVM classifier?
A. The margin definition in the SVM formulation can be considered as a regu-
larization term to prevent overfitting.
B. Any function can be used a kernel function.
C. Using a valid kernel, an SVM classifier can be trained without knowing the
feature values for each sample.
D. The so-called ""support vectors"" refer to the positive and negative planes.
2. Which of the following statement(s) is/are true regarding the decision tree
classifier?
A. When training a decision tree classifier, the depth of the tree goes linearly
with respect to the number of training samples.
B. The training objective function for a decision tree classifier has in general no
analytic form to optimize for.
C. Tree pruning can be used to prevent overfitting.
D. In general, the deeper a decision tree is, the more complex the decision
boundary is.

1 (10 points) Multiple Choice
1. Which of the following statement(s) is/are true regarding the SVM classifier?
A. The margin definition in the SVM formulation can be considered as a regu-
larization term to prevent overfitting.
B. Any function can be used a kernel function.
C. Using a valid kernel, an SVM classifier can be trained without knowing the
feature values for each sample.
D. The so-called ""support vectors"" refer to the positive and negative planes.
2. Which of the following statement(s) is/are true regarding the decision tree
classifier?
A. When training a decision tree classifier, the depth of the tree goes linearly
with respect to the number of training samples.
B. The training objective function for a decision tree classifier has in general no
analytic form to optimize for.
C. Tree pruning can be used to prevent overfitting.
D. In general, the deeper a decision tree is, the more complex the decision
boundary is.

Added by Francisco W.

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