CACS486 Machine Learning

Machine Learning old question papers

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10 questionsSit this paper (timed)

Tribhuvan University

Bachelor of Computer Application

Semester 8 · TU Board 2025

Course Title: Machine Learning (CACS486)

Full Marks: 60Pass Marks: 24Time: 3 hours

Candidates are required to answer the question in their own words as far as possible.

Group B

Attempt any SIX question.(6 × 5 = 30)

  1. 2.

    Explain the bias–variance trade-off in machine learning. Provide examples of how increasing model complexity affects both bias and variance. [2+3]

    5
  2. 3.

    Describe the decision tree algorithm. What are the advantages and disadvantages of using decision trees? [3+2]

    5
  3. 4.

    Discuss the structure and learning process of a neural network in supervised learning.

    5
  4. 5.

    The ages of 10 employees in a company are: [2.5+2.5] 22, 25, 29, 30, 31, 33, 35, 36, 40, 45 a. Find minimum, Q1, median, Q3, and maximum values. b. Draw a box plot for the data.

    5
  5. 6.

    A spam filter uses the words "offer" and "win" as features. From training data: ClassP(offer)P(win)Prior ProbabilitySpam0.80.60.4Not Spam0.10.050.6 Given an email containing both words, classify it as Spam or Not Spam using Naïve Bayes.

    5
  6. 7.

    Explain the steps of the Principal Component Analysis (PCA) algorithm.

    5
  7. 8.

    Perform one iteration of K-means clustering for the following points with initial centroids C1 = (1,1) and C2 = (5,4): Points: (1,1), (2,1), (4,3), (5,4). Use Euclidean distance.

    5

Group C

Attempt any TWO questions(2 × 10 = 20)

  1. 9.

    Describe the Support Vector Machine (SVM) concept of maximum margin classification with a diagram. A model trained on a dataset achieves the following results: Training accuracy: 95% Validation accuracy: 80% Test accuracy: 78% Discuss whether the model is underfitting, overfitting, or well-fitted. Justify your answer. [6+4]

    10
  2. 10.

    Explain the concept of Confusion Matrix with example. Given the dataset below, fit a linear regression model using the least squares method and find the best-fit line equation. Also find the value of Y at X=4.5 [3+7] XY1224354658

    10
  3. 11.

    Explain the K-nearest neighbor (KNN) algorithm. How does the value of k affect bias and variance? Given two classes of points: Class +1: (2, 2), (4, 4) Class −1: (4, 0), (0, 0) Determine the equation of the separating hyper-plane. [6+4]

    10

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