UNIT 3
Support Vector Machines
MCQs
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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?
- True
- False
Ans: A
3. Support Vector Machines are not sensitive to
feature scaling
- 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 linearly
separable
- All of the above
Ans: D
6. The objectives of Soft Margin Classification are
to find a good balance between
- Keeping the street as large as possible
- Limiting the margin violations
- Both of the above
- None of the above
Ans: C
7. The balance between keeping the street as large as
possible and limiting margin violations is controlled by this hyperparameter
- Tol
- Loss
- Penalty
- C
Ans: D
8. A smaller
C value leads to a wider street but more margin violations.
- True
- False
Ans: A
9. If your SVM model is overfitting, you can try
regularizing it by reducing the value of
- Tol
- C hyperparameter
- intercept_scaling
- None of the above
Ans: B
10. Problems
with adding polynomial features are
- At a low polynomial degree, it cannot deal with very complex
datasets
- With a high polynomial degree, it creates a huge number of features
- Adding high polynomial degree makes the model too slow
- All of the above
Ans: D
11. The
hyperparameter coef0 of SVC controls how much the model is influenced by
high-degree polynomials versus low-degree polynomials
A. True
B. False
Ans: A
12. A
similarity function like Gaussian Radial Basis Function is used to
A.
Measure how many features are related to each
other
B.
Find the most important features
C.
Find the relationship between different
features
D.
Measure how much each instance resembles a
particular landmark
Ans: D
13. When
adding features with similarity function, and creating a landmark at the
location of each and every instance in the training set, a training set with m
instances and n features gets transformed to (assuming you drop the original
features)
- A training set with n instances and n features
- A training set with m/2 instances and n/2 features
- A training set with m instances and m features
- A training set with m instances and n features
Ans: C
14. When using SVMs we can apply an almost miraculous
mathematical technique for adding polynomial features and similarity features
called the
- Kernel trick
- Shell trick
- Mapping and Reducing
- None of the above
Ans: A
15. Which is right for the gamma parameter of SVC
which acts as a regularization hyperparameter
- If model is overfitting, increase it, if it is underfitting, reduce
it
- If model is overfitting, reduce it, if it is underfitting, increase
it
- If model is overfitting, keep it same
- If it is underfitting, keep it same
Ans: B
16. LinearSVC
is much faster than SVC(kernel="linear"))
- True
- False
Ans: A
17. In SVM
regression the model tries to
- Fit the largest possible street between two classes while limiting
margin violations
- Fit as many instances as possible on the street while limiting
margin violations
- Both
- None of the above
Ans: B
18. The SVR
class is the regression equivalent of the SVC class, and the LinearSVR class is
the regression equivalent of the LinearSVC class
- True
- False
Ans: A
