Question 180 MCQ · 4.0 marks
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Machine Learning Techniques · Quiz 1 · Jan 2024
Machine Learning Techniques · Quiz 1 · Jan 2024
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Question 181 MCQ · 4.0 marks
Yes
No
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Question 182 MCQ · 4.0 marks
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Question 183 MCQ · 4.0 marks
Consider a dataset consisting of 1000 samples in a 20-dimensional space. You apply Kernel PCA
with a polynomial kernel of degree 3 to reduce the dimensionality of the data. Which of the
following statements regarding Kernel PCA is true?
Kernel PCA always results in a lower-dimensional representation of the data
compared to standard PCA.
Kernel PCA can only be applied to datasets that are linearly separable.
Choosing a higher degree polynomial kernel in Kernel PCA can lead to
increased flexibility in capturing non-linear relationships.
None of these.
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Question 184 NAT · 4.0 marks
Based on the above data, answer the given subquestions.
What is the value of a? Enter your answer correctly to three decimal places.
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Question 185 NAT · 4.0 marks
Based on the above data, answer the given subquestions.
What is the value of b? Enter your answer correctly to two decimal places.
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Question 186 MCQ · 4.0 marks
Consider the data points shown in the following image:
Based on the above data, answer the given subquestions.
Perform K-means clustering with K = 2 and initial cluster centers at (0, 0) and (6, 0). What are the
final means of clusters after convergence?
(2, 0) and (8, 0)
(2, 0) and (8.5, 0)
(0, 0) and (8, 0)
(0, 0) and (8.5, 0)
None of these
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Question 187 NAT · 4.0 marks
Consider the data points shown in the following image:
Based on the above data, answer the given subquestions.
After introducing a new data point (7, 0) , the cluster centers were updated. Enter the sum of the
updated x-coordinates for both cluster centers accurately, rounding your answer to two decimal
places.
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Question 188 MCQ · 2.0 marks
Based on the above data, answer the given subquestions.
What is the log-likelihood of this dataset under this distribution? ln represents the natural
logarithm or loge.
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Question 189 NAT · 2.0 marks
Based on the above data, answer the given subquestions.
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Question 190 MSQ · 5.0 marks
Consider Lloyd’s algorithm used for k-means clustering and choose the correct statements:
K-means algorithm may get stuck at local minima.
It guarantees finding the optimal clustering (global minimum) in every run.
In practice, k should be as large as possible.
If the resources are limited and the data set is huge, it will be good to prefer K-
means over K-means++.
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Question 191 MSQ · 5.0 marks
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Question 192 NAT · 4.0 marks
Consider a dataset with 100 total data points. Each data point is classified as either type A or type
B. We model this using a Bernoulli distribution, where p is the probability of a data point being
type A. If the maximum likelihood estimate (MLE) of p based on the dataset is 0.4, how many data
points of type B are there in this dataset?
A published solution is not available for this question yet.