6406532306902. The Vuex getters are not reactive.
MLT
Section Id : 64065348510
Section Number : 12
Section type : Online
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Number of Questions : 12
Number of Questions to be attempted : 12
Section Marks : 50
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Yes
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Sub-Section Number : 1
Sub-Section Id : 640653100858
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Question Number : 178 Question Id : 640653689595 Question Type : MCQ Is Question
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Correct Marks : 0
Question Label : Multiple Choice Question
THIS IS QUESTION PAPER FOR THE SUBJECT "DIPLOMA LEVEL : MACHINE LEARNING
TECHNIQUES (COMPUTER BASED EXAM)"
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CROSS CHECK YOUR HALL TICKET TO CONFIRM THE SUBJECTS TO BE WRITTEN.
(IF IT IS NOT THE CORRECT SUBJECT, PLS CHECK THE SECTION AT THE TOP FOR THE SUBJECTS
REGISTERED BY YOU)
Options :
6406532306909. YES
6406532306910. NO
Sub-Section Number : 2
Sub-Section Id : 640653100859
Question Shuffling Allowed : No
Is Section Default? : null
Question Id : 640653689596 Question Type : COMPREHENSION Sub Question Shuffling
Allowed : No Group Comprehension Questions : No Question Pattern Type : NonMatrix
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Question Numbers : (179 to 181)
Question Label : Comprehension
Sub questions
Question Number : 179 Question Id : 640653689597 Question Type : SA Calculator : None
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Correct Marks : 4
Question Label : Short Answer Question
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Question Number : 180 Question Id : 640653689598 Question Type : MSQ Is Question
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Time : 0
Correct Marks : 4 Max. Selectable Options : 0
Question Label : Multiple Select Question
Options :
6406532306912. The kernel regression is parametric
6406532306913. The kernel regression is non-parametric
6406532306914. The Kernel matrix K is positive semi-definite
6406532306915. In general, Kernel regression is computationally expensive than a simple linear
regression
Question Number : 181 Question Id : 640653689599 Question Type : SA Calculator : None
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Correct Marks : 4
Question Label : Short Answer Question
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Evaluation Required For SA : Yes
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Text Areas : PlainText
Possible Answers :
39 to 40
Sub-Section Number : 3
Sub-Section Id : 640653100860
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Is Section Default? : null
Question Number : 182 Question Id : 640653689600 Question Type : MCQ Is Question
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Correct Marks : 4
Question Label : Multiple Choice Question
Options :
6406532306917. (1.05, 1.20)
6406532306918. (0.85, 0.95)
6406532306919. (1,1)
6406532306920. (3, 3.5)
Sub-Section Number : 4
Sub-Section Id : 640653100861
Question Shuffling Allowed : Yes
Is Section Default? : null
Question Number : 183 Question Id : 640653689601 Question Type : MSQ Is Question
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Correct Marks : 4 Max. Selectable Options : 0
Question Label : Multiple Select Question
Which of the following is/are the primary advantages of using L1 regularization
Options :
6406532306921. L1 regularization reduces the risk of overfitting.
6406532306922. L1 regularization tends to produce sparse models.
6406532306923. L1 regularization always improves the model’s predictive accuracy on large
datasets.
6406532306924. L1 regularization is primarily used to increase model complexity.
Question Number : 184 Question Id : 640653689605 Question Type : MSQ Is Question
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Time : 0
Correct Marks : 4 Max. Selectable Options : 0
Question Label : Multiple Select Question
Select all the statements that are true about decision trees and k-Nearest Neighbors (k-NN) in
machine learning:
Options :
6406532306935. Decision trees are a supervised learning algorithm used for classifications.
6406532306936. The k-NN algorithm is a lazy learner, which means it doesn’t build an explicit
model during the training phase.
6406532306937. In k-NN, the value of k represents the number of features used for
classification.
6406532306938. k-Nearest Neighbors (k-NN) is a parametric model that requires estimating
probability distributions.
6406532306939. The depth of the tree is a hyperparameter and is typically chosen using cross-
validation.
Sub-Section Number : 5
Sub-Section Id : 640653100862
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Question Number : 185 Question Id : 640653689602 Question Type : MCQ Is Question
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Correct Marks : 4
Question Label : Multiple Choice Question
Options :
6406532306925. w1 → Lasso regression, w2 → Linear regression, w3 → Ridge regression
6406532306926. w1→ Ridge regression, w2 → Lasso regression, w3 → Linear regression
6406532306927. w1 → Linear regression, w2 → Ridge regression, w3 → Lasso regression
6406532306928. w1 → Ridge regression, w2 → Linear regression, w3 → Lasso regression
Question Number : 186 Question Id : 640653689603 Question Type : MCQ Is Question
Mandatory : No Calculator : None Response Time : N.A Think Time : N.A Minimum Instruction
Time : 0
Correct Marks : 4
Question Label : Multiple Choice Question
Options :
6406532306929. β = [0.75, 0.75]
6406532306930. β = [1, 0.5]
6406532306931. β = [0.5, 1]
6406532306932. β = [0.85, 0.12]
6406532306933. None of these
Sub-Section Number : 6
Sub-Section Id : 640653100863
Question Shuffling Allowed : Yes
Is Section Default? : null
Question Number : 187 Question Id : 640653689604 Question Type : SA Calculator : None
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Correct Marks : 4
Question Label : Short Answer Question
You are working on a decision tree algorithm to classify whether a bank loan applicant will default
on their loan based on several financial factors. The dataset includes Credit Score, Annual Income,
Loan Amount, Loan Term as features.
The target variable is binary: 1 for ”Default” and 0 for ”No Default.”
You have a dataset of 500 loan applicants, and you want to construct a decision tree to predict
loan default. To determine the first split (root node), you'll use the information gain as the
criterion. Here’s the distribution of loan default in the dataset:
Default: 150 applicants No Default: 350 applicants
Calculate the Entropy for the initial dataset.
Response Type : Numeric
Evaluation Required For SA : Yes
Show Word Count : Yes
Answers Type : Range
Text Areas : PlainText
Possible Answers :
0.83 to 0.92
Question Number : 188 Question Id : 640653689608 Question Type : SA Calculator : None
Response Time : N.A Think Time : N.A Minimum Instruction Time : 0
Correct Marks : 4
Question Label : Short Answer Question
Response Type : Numeric
Evaluation Required For SA : Yes
Show Word Count : Yes
Answers Type : Equal
Text Areas : PlainText
Possible Answers :
8191
Sub-Section Number : 7
Sub-Section Id : 640653100864
Question Shuffling Allowed : Yes
Is Section Default? : null
Question Number : 189 Question Id : 640653689606 Question Type : SA Calculator : None
Response Time : N.A Think Time : N.A Minimum Instruction Time : 0
Correct Marks : 2
Question Label : Short Answer Question
Given a training dataset with 100 data points, how many distances would we have to compute in
the process of predicting the label of test-point in the k-NN algorithm with k = 5
Response Type : Numeric
Evaluation Required For SA : Yes
Show Word Count : Yes
Answers Type : Equal
Text Areas : PlainText
Possible Answers :
100
Question Number : 190 Question Id : 640653689607 Question Type : SA Calculator : None
Response Time : N.A Think Time : N.A Minimum Instruction Time : 0
Correct Marks : 2
Question Label : Short Answer Question
If the proportion of points belonging to class 1 in a node is p, for what value of p is the node’s
entropy maximum?
Response Type : Numeric
Evaluation Required For SA : Yes
Show Word Count : Yes
Answers Type : Equal
Text Areas : PlainText
Possible Answers :
0.5
Sub-Section Number : 8
Sub-Section Id : 640653100865
Question Shuffling Allowed : No
Is Section Default? : null
Question Id : 640653689609 Question Type : COMPREHENSION Sub Question Shuffling
Allowed : No Group Comprehension Questions : No Question Pattern Type : NonMatrix
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Question Numbers : (191 to 192)
Question Label : Comprehension
Sub questions
Question Number : 191 Question Id : 640653689610 Question Type : SA Calculator : None
Response Time : N.A Think Time : N.A Minimum Instruction Time : 0
Correct Marks : 3
Question Label : Short Answer Question
Response Type : Numeric
Evaluation Required For SA : Yes
Show Word Count : Yes
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Question Number : 192 Question Id : 640653689611 Question Type : SA Calculator : None
Response Time : N.A Think Time : N.A Minimum Instruction Time : 0
Correct Marks : 3
Question Label : Short Answer Question
What will be the predicted label according to the Naive Bayes decision rule?
Response Type : Numeric
Evaluation Required For SA : Yes
Show Word Count : Yes
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MLP
Section Id : 64065348511
Section Number : 13
Section type : Online
Mandatory or Optional : Mandatory
Number of Questions : 24
Number of Questions to be attempted : 24
Section Marks : 50
Display Number Panel : Yes
Group All Questions : No
Enable Mark as Answered Mark for Review and
Yes
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Maximum Instruction Time : 0
Sub-Section Number : 1
Sub-Section Id : 640653100866
Question Shuffling Allowed : No
Is Section Default? : null
