Machine Learning MCQ : Test 6

Explore this diverse selection of multiple-choice questions (MCQs) designed for various examinations. Machine Learning MCQ : Test 6 focuses on essential aspects of the subject, ensuring comprehensive preparation across different categories and fields of study to enhance your knowledge and readiness. The right answers for each question is provided next to respective questions for your convenience, you can either attend the test or dirtectly access the right answers by clicking the show correct answer button.

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1. Which algorithm is used for association rule mining?

A. Decision tree

B. Apriori

C. Linear regression

D. K-means

2. What is the main advantage of using a hierarchical clustering algorithm?

A. High computational efficiency

B. Ability to determine the number of clusters automatically

C. Simple implementation

D. Reduced model complexity

3. Which method is used for named entity recognition (NER) in NLP?

A. Linear regression

B. Hidden Markov Model (HMM)

C. K-means clustering

D. Decision tree

4. How does the backpropagation algorithm work?

A. By clustering data points

B. By updating weights based on the gradient of the loss function

C. By reducing data size

D. By increasing model complexity

5. Which technique is used to reduce the dimensionality of sparse data?

A. Principal component analysis (PCA)

B. Singular value decomposition (SVD)

C. Linear regression

D. Decision tree

6. What is the purpose of using ensemble methods?

A. To increase data size

B. To improve model performance by combining multiple models

C. To reduce model complexity

D. To cluster data points

7. Which algorithm is commonly used for collaborative filtering?

A. K-means clustering

B. Decision tree

C. Matrix factorization

D. Logistic regression

8. How does a variational autoencoder (VAE) differ from a traditional autoencoder?

A. VAE uses a probabilistic approach to generate new data

B. VAE reduces data dimensions

C. VAE clusters data points

D. VAE increases model complexity

9. Which method is used for text generation in NLP?

A. Decision tree

B. Recurrent neural network (RNN)

C. K-means clustering

D. Random forest

10. How does a Siamese network work?

A. By clustering data points

B. By comparing two inputs through shared weights

C. By reducing data size

D. By increasing model complexity

11. Which evaluation metric is used for ranking problems?

A. Precision

B. Recall

C. Mean reciprocal rank (MRR)

D. F1 score

12. What is the main challenge in training a GAN?

A. High computational cost

B. Balancing the generator and discriminator training

C. Reducing data dimensions

D. Increasing model complexity

13. Which method is used for speech recognition?

A. Decision tree

B. Hidden Markov Model (HMM)

C. K-means clustering

D. Random forest

14. How does the Transformer architecture differ from RNNs?

A. Transformer uses attention mechanisms

B. Transformer clusters data points

C. Transformer reduces data size

D. Transformer increases model complexity

15. What is the purpose of a reinforcement learning agent's policy?

A. To reduce data dimensions

B. To define the agent's behavior

C. To cluster data points

D. To increase model complexity

16. Which method is used to handle high cardinality categorical features?

A. One-hot encoding

B. Embedding layers

C. Principal component analysis (PCA)

D. Decision tree

17. How does the gradient clipping technique help in training RNNs?

A. By reducing data dimensions

B. By preventing exploding gradients

C. By clustering data points

D. By increasing model complexity

18. What is the main advantage of using a capsule network?

A. High computational efficiency

B. Ability to capture spatial hierarchies

C. Simple implementation

D. Reduced model complexity

19. Which algorithm is used for outlier detection?

A. K-means clustering

B. Isolation forest

C. Logistic regression

D. Decision tree

20. How does an attention mechanism improve sequence modeling?

A. By clustering data points

B. By focusing on relevant parts of the input sequence

C. By reducing data dimensions

D. By increasing model complexity

21. Which method is used for dimensionality reduction in high-dimensional data?

A. Linear regression

B. t-Distributed Stochastic Neighbor Embedding (t-SNE)

C. Decision tree

D. K-means clustering

22. What is the main purpose of using a variational autoencoder (VAE)?

A. To reduce data dimensions

B. To generate new data samples

C. To cluster data points

D. To increase model complexity

23. How does a reinforcement learning agent learn?

A. By clustering data points

B. By receiving rewards and penalties from the environment

C. By reducing data dimensions

D. By increasing model complexity

24. Which algorithm is used for hierarchical clustering?

A. K-means clustering

B. Agglomerative clustering

C. Decision tree

D. Logistic regression

25. How does dropout prevent overfitting in neural networks?

A. By clustering data points

B. By randomly dropping units during training

C. By reducing data dimensions

D. By increasing model complexity

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