Question $1$:

In a decision tree, what is typically found at a leaf node?

a$)$ A feature test condition

b$)$ The median value of a feature

c$)$ Final prediction or classification

d$)$ Decision rule for further branching

Explain your answer

Question $2$:

Hard margin SVMs differ from soft margin SVMs in that:

a$)$ Hard margin SVMs are designed for nonlinearly separable data

b$)$ Hard margin SVMs allow data points to be misclassified, while soft margin does not

c$)$ Soft margin SVMs do not consider the margin between classes

d$)$ Soft margin SVMs allow data points to be misclassified, while hard margin does not

Explain your answer

Question $3$:

Decision trees are prone to overfitting. What is one common way to avoid overfitting in decision trees?

a$)$ Applying a more complex decision tree algorithm

b$)$ Regularizing by restricting the maximum depth or other hyperparameters

c$)$ Allows the tree to adapt freely on the training data

d$)$ Increasing the maximum depth of tree

Explain your answer

Question $4$:

What is the primary benefit of using the kernel trick compared to adding polynomial features to a model?

a$)$ The kernel trick avoids the computational complexity of adding a large number of polynomial features.

b$)$ The kernel trick adds more complexity to the model compared to adding polynomial features.

c$)$ Polynomial features always result in better model accuracy.

d$)$ Adding polynomial features significantly improves model interpretability.

Explain your answer

Question $5$:

What is the primary objective of an SVM classifier?

a$)$ Minimizes the margin between two classes.

b$)$ Fits as narrow of a street as possible between two classes.

c$)$ Maximizes the margin between two classes.

d$)$ Minimizes the margin between two classes.

Explain your answer

Question $6$:

Machine learning models that are intuitive, easy to interpret, and provide simple rules may be called:

a$)$ Neural networks

b$)$ Black box models

c$)$ Brown box models

d$)$ White box models

Explain your answer