5CSM1 ML LAB QUIZ B-1

1. How can SVM be classified?

• 1. a) It is a model trained using supervised learning. It can be used for classification and not for regression.

• 1. b) It is a model trained using unsupervised learning. It can be used for classification and but not for regression.

• 1. c) It is a model trained using supervised learning. It can be used for classification and regression.

• 1. d) It is a model trained using unsupervised learning.It can be used for classification and regression.

2. What is the purpose of the Kernel Trick?

• a)To transform the problem from nonlinear to linear

• b)To transform the problem from regression to classification

• c)To transform the data from nonlinearly separable to linearly separable

• d)To transform the problem from supervised to unsupervised learning.

3. Which of the following statements is not true about SVM?

• 
  a) It is memory efficient
  
  

• b) It can address a large number of predictor variables
  
  
  
  

• c) It is versatile

• d) It doesn’t require feature scaling

In SVM, if the number of input features is 2, then the hyperplane is a _____.

• a)line

• b)circle

• c)plane

• d)none of these

5.For SVM, which options are correct?

• (A) Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane
  
  

• (B) Support vectors are data points that are far away from the hyperplane and influence the position and orientation of the hyperplane
  
  

• (C) Deleting the support vectors won’t change the position of the hyperplane

• D . All of the above

6.What’s the objective of the support vector machine algorithm?

• (A) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of features.
  
  

• (B) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of samples.
  
  

• (C) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of target variables.
  
  

• (D) None of these

7. In K-means clustering, what is the purpose of the silhouette score?

• a)To measure the compactness of clusters

• b)To measure the separation between clusters

• c)To determine the optimal number of clusters

• d)Both A and B

8. In K-means clustering, which of the following factors can impact the quality of the clustering solution?

• a)The number of clusters

• b)The distance metric used

• c)The initialization method

• d)All of the above

9. In K-means clustering, how are initial centroids typically selected?

• a)Randomly from the data points

• b)By using the K-means++ initialization method

• c)By employing a separate clustering algorithm

• d)Both A and B

10. In K-means clustering, what does the term "convergence" refer to?

• a)The point at which the centroids stop changing significantly

• b)The point at which the algorithm has found the optimal number of clusters

• c)The point at which the algorithm has found the minimum number of  datapoints in a cluster

   

• d)The point at which the algorithm has found the best distance metric