๐ Chandigarh University โ MCA/CA/B.Tech
These Mid-Semester papers are part of the official examinations of Chandigarh University,
designed to test knowledge and practical understanding of core MCA, Computer Applications, and B.Tech subjects.
๐ก Each paper encourages students to think critically, apply concepts, and showcase problem-solving skills โ helping them prepare for real-world IT challenges.
Usage Condition:
- ๐ Papers are for reference and study purposes only.
- ๐งโ๐ Students should use them responsibly and not for any malpractice.
- ๐ Availability depends on the course and year.
Machine Learning
Subject Code:24CAH-703
Semester: 2 | Time: 1 Hour | Max Marks: 20
Section A
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Explain the concept of entropy in information theory and provide its mathematical expression.
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Define the concept of a hyperplane in n-dimensional space. Also determine the advantages of maximizing the margin.
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Explain for which scenario you would prefer F1-score over accuracy as an evaluation metric. Justify your answer with an example.
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Mention the reason why mean squared error is not suitable for logistic regression.
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State the mathematical formula for Euclidean distance used in KNN with graph representation.
Section B
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Discuss the role of Information Gain in determining the optimal splits in a decision tree. In what way does it influence the construction of the tree?
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Compare KNN with Logistic Regression in terms of decision boundaries, assumptions, and performance.
