
Quiz2
Authored by Aarushi khatri
Instructional Technology
University
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Support Vector Machines are primarily designed for:
Regression problems to predict continuous values.
Unsupervised learning tasks like clustering data points.
Classification problems to separate data points into distinct classes.
Both regression and classification tasks equally well.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is NOT an advantage of using SVMs?
Effective in high-dimensional spaces due to the 'curse of dimensionality' being less impactful.
Efficient for prediction once the model is trained.
Requires careful selection and tuning of hyperparameters.
Generally performs well with small datasets.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of SVMs, what are 'support vectors'?
Randomly chosen data points used to define the hyperplane.
Data points closest to the hyperplane that define the margin.
Data points that are misclassified by the model.
All data points used to train the SVM.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A high value of the regularization parameter (C) in an SVM indicates:
A more complex model with a smaller margin but potentially better handling of outliers.
A simpler model with a larger margin but stricter penalty for misclassification.
There is no impact on the model complexity.
The model will always overfit the training data.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The primary objective of a Support Vector Machine (SVM) is to:
Minimize the distance between data points and the hyperplane.
Maximize the variance of the data points.
Find the hyperplane with the largest number of data points on it.
Minimize the error on the training data, regardless of margins.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What type of kernel function is typically used in SVMs for non-linearly separable data?
Linear kernel
Polynomial kernel
Sigmoid kernel
All of the above (depending on the scenario)
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
SVMs are generally considered to be:
Highly interpretable due to the clear decision boundary.
Prone to overfitting, especially with high-dimensional data.
Not suitable for multi-class classification problems.
All of the above.
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