WB JECA MCA Machine Learning Previous Year Question Paper and Solution with PDF

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WB JECA MCA Previous Year Questions (PYQs)

WB JECA MCA Machine Learning PYQ

Qus : 1 WB JECA MCA PYQ

In machine learning, VC dimension is used to measure the capacity of a set of functions.

VC means __________.

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2023 PYQ

Solution

VC dimension stands for Vapnik–Chervonenkis dimension, a measure of model capacity/complexity.

Qus : 2 WB JECA MCA PYQ

Artificial Neural networks are __________ algorithms.

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Solution

Artificial Neural Networks (ANNs) are computational algorithms inspired by biological neural networks.

Qus : 3 WB JECA MCA PYQ

One-class SVM is __________ algorithm.

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2023 PYQ

Solution

One-class SVM is mainly used for anomaly/outlier detection, which falls under unsupervised learning.

Qus : 4 WB JECA MCA PYQ

Time complexity of K-Means clustering is __________.

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2023 PYQ

Solution

For K-Means, the time complexity per iteration is O(kN), where k = number of clusters and N = number of data points.

Qus : 5 WB JECA MCA PYQ

Forward–backward algorithm is used in case of HMM to compute __________ of all hidden state variables.

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2023 PYQ

Solution

The forward–backward algorithm in Hidden Markov Models (HMM) is used to compute posterior marginals over hidden states.

Qus : 6 WB JECA MCA PYQ

HMM is a specific instance of CRF which is known as __________.

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2023 PYQ

Solution

Hidden Markov Model (HMM) is a probabilistic model and can be seen as a special case of Conditional Random Fields (CRF).

Qus : 7 WB JECA MCA PYQ

In FP-Growth Algorithm, FP means __________

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2023 PYQ

Solution

In FP-Growth, FP stands for Frequent Pattern, used in association rule mining.

Qus : 8 WB JECA MCA PYQ

An artificially intelligent car decreases its speed based on its distance from the car in front of it. Which method is used?

(A) Naive Bayes
(B) Decision Tree
(C) Linear Regression
(D) Logistic Regression

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2024 PYQ

Solution

The car’s speed is a continuous variable that depends on the distance from the car ahead.
To model this relation, we need a method for predicting a continuous outcome.

Linear Regression is used because it maps distance (input) to speed (output).

Correct Answer: (C) Linear Regression

Qus : 9 WB JECA MCA PYQ

K-means clustering method is an example of which type of clustering method?

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2024 PYQ

Solution

K-means clustering is a partitioning method because it divides the dataset into k clusters based on distance.
Answer = (D) Partitioning

Qus : 10 WB JECA MCA PYQ

Regression is a type of:

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2024 PYQ

Solution

Solution:
- Regression uses labeled data to predict continuous values.
- It belongs to Supervised Learning.
Answer = (A) Supervised learning

Qus : 11 WB JECA MCA PYQ

What is the purpose of the confusion matrix in machine learning?

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2024 PYQ

Solution

Solution:
- Confusion matrix is used in classification tasks.
- It compares predicted vs actual labels (TP, FP, TN, FN).
Answer = (C) To evaluate the performance of a classification model

Qus : 12 WB JECA MCA PYQ

Which of the following is an example of a classification problem?

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2024 PYQ

Solution

Solution: Classification predicts categories. Customer churn = Yes/No ⇒ classification.
Answer: (C) Predicting whether a customer will churn or not

Qus : 13 WB JECA MCA PYQ

What is the difference between supervised and unsupervised learning?

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WB JECA MCA Previous Year PYQ WB JECA MCA JECA MCA 2024 PYQ

Solution

Solution: Supervised = labelled data, Unsupervised = unlabelled data.
Answer: (A) Supervised learning requires labelled data while unsupervised learning does not.

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