cs2007_2025T2_Q1_AN.pdf

Business Data Management(BDM) · Quiz 1 · May 2025

Questions and published explanations below are available without starting a test. Some questions may not have a published solution yet.

Question 127 MCQ · 3.0 marks

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baseClass, highlighted, btn-disabled **MLT** **Section Id :** 64065392560 **Section Number :** 9 **Section type :** Online **Mandatory or Optional :** Mandatory **Number of Questions :** 13 **Number of Questions to be attempted :** 13 **Section Marks :** 40 **Display Number Panel :** Yes **Section Negative Marks :** 0 **Group All Questions :** No **Enable Mark as Answered Mark for Review and** No **Clear Response :** **Section Maximum Duration :** 0 **Section Minimum Duration :** 0 **Section Time In :** Minutes **Maximum Instruction Time :** 0

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Question 129 MCQ · 2.0 marks

How does K-means++ algorithm enhance the initialization process as compared to the standard K- means algorithm?

It ensures the algorithm converges in a fixed number of iterations.
It improves centroid initialization by spreading them out, leading to better separated clusters.
It automatically calculates the best value of K.
It removes the need to run the algorithm multiple times.

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Question 130 MCQ · 2.0 marks

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Question 131 MCQ · 4.0 marks

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Question 132 MCQ · 4.0 marks

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Question 133 MCQ · 3.0 marks

You are presented with a dataset that has latent variables that influences your data. You are asked to use Expectation-Maximization algorithm to best capture the data. How would you define the steps in the EM (Expectation and Maximization) algorithm?

E-step: Estimate the latent variables in the dataset, M-step: Maximize the likelihood over the model parameters.
E-step: Estimate the number of latent variables in the dataset, M-step: Maximize the likelihood over the model parameters.
E-step: Estimate the likelihood over the model parameters, M-step: Maximize the number of latent variables in the dataset.
E-step: Estimate the likelihood over the model parameters, M-step: Maximize the number of parameters in the model.

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Question 134 MSQ · 3.0 marks

Which of the following statements is/are true?

EM algorithm converges to a global optimum.
Lloyd’s algorithm converges to a local optimum.
Poor initialization does not affect the clusters produced by EM algorithm.
Poor initialization leads to a slower convergence of Lloyd’s algorithm.
Lloyd’s algorithm is sensitive to the number of clusters (k).

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Question 135 MSQ · 3.0 marks

For a linear regression problem, which among the following are true? Select all that apply.

Gradient descent will converge to a global minimum.
Gradient descent will converge to a local minimum (which is not a global minimum).
Stochastic gradient descent is guaranteed to converge to a global minimum.
Gradient descent will take lesser number of iterations to converge to a local minimum than stochastic gradient descent.

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