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/ Interview Guides / Machine Learning MCQ

Machine Learning MCQ

Last Updated: Jan 08, 2024
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Machine learning is the Science of Getting computers to learn without being explicitly programmed.  Machine learning works on a simple concept that is understanding with experiences.

The primary aim of machine learning is to allow computers to learn automatically without human interaction.

Scope of machine learning

  • Machine learning in education
  • Machine learning in search engine
  • Machine learning in digital marketing
  • Machine learning in Healthcare
  • Spam protector
  • Traffic alert
  • Social media
  • Google Translate

 Limitation of machine learning

  • Accuracy depends on training and learning which is not always available.
  • It requires large data sets to learn about various topics which might be time taking and require various resources
  • Good performance cannot always be guaranteed.
  • a Mason needs to have heterogeneity in the data set to learn meaningful Insight.

Types of machine learning

  • Supervised learning:
    • in supervised learning, given training explain examples of Input and corresponding output, the machine can predict outputs for new inputs
    • in supervised learning, we train the images with respect to data that is well labeled and with the correct output
  • Unsupervised learning:
    • Unsupervised learning deals with the unlabeled data
    • No training data set is provided which means, no training will be given to the machine. Therefore it must work on its own to discover the required information.
    • The machine is trained with unlabelled data.

Linear regression

  • Linear regression is a machine learning algorithm based on supervised learning
  • It is the easiest and most popular machine learning algorithm
  • It is used for predictive analysis
  • It makes prediction for continuous variable size price of a product or house of salary
  • Regression models target prediction values based upon their independent variables.

Artificial Neural Network (ANN)

  • An artificial neural network is a computational nonlinear model that is inspired by the brain.
  • ANN  can perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples.
  • It consists of a large collection of artificial neurons of the processing element which operates in parallel
  • ANNs Are capable of learning, which takes place by altering values.

Machine Learning MCQs

1. 

Among the following option identify the one which is not a type of learning

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2. 

Identify the kind of learning algorithm for  “facial identities for facial expressions”.

3. 

Identify the model which is trained with data in only a single batch.

4. 

What is the application of machine learning methods to a large database called?

5. 

Identify the type of learning in which labeled training data is used.

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6. 

Identify whether true or false:  In PCA the number of input dimensions is equal to principal components.

7. 

Among the following identify the one in which dimensionality reduction reduces.

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8. 

Which of the following machine learning algorithm is based upon the idea of bagging?

9. 

Choose a disadvantage of decision trees among the following.

10. 

What is the term known as on which the machine learning algorithms build a model based on sample data?

11. 

Machine learning is a subset of which of the following.

12. 

Which of the following machine learning techniques helps in detecting the outliers in data?

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13. 

The father of machine learning is _____________

14. 

The most significant phase in genetic algorithm is _________

15. 

Which of the following are common classes of problems in machine learning?

16. 

Among the following options identify the one which is false regarding regression.

17. 

Identify the successful applications of ML.

18. 

Identify the incorrect numerical functions in the various function representation of machine learning.

19. 

FIND-S algorithm ignores?

20. 

Select the correct definition of neuro software.

21. 

Choose whether the following statement is true or false:  The backpropagation law is also known as the generalized Delta rule.

22. 

Choose the general limitations of the backpropagation rule among the following.

23. 

Analysis of ML algorithm needs

24. 

Choose the most widely used mattress and tools to assess the classification models.

25. 

Full form of PAC is _____________

26. 

Choose that following statement is true or false: True error is defined over the entire instance space, and not just over training data

27. 

Choose the options below of which the area CLT is comprised of.

28. 

Choose the instance-based learner.

29. 

Identify the difficulties with the k-nearest neighbor algorithm.

30. 

The total types of the layer in radial basis function neural networks is ______

31. 

 Which of the following is an application of CBR?

32. 

Choose the correct advantages of CBR.

33. 

Machine learning as various Search and Optimisation algorithms. Identify among the following which is not evolutionary computation.

34. 

Choose whether the following statement is true or false: Artificial intelligence is the process that allows a computer to learn and make decisions like humans.

35. 

Which of the following is not machine learning disciplines?

36. 

What does K stand for in K mean algorithm?

37. 

Choose whether true or false:  Decision tree cannot be used for clustering

38. 

 Identify the clustering method which takes care of variance in data

39. 

Which of the following is not a supervised learning

40. 

What is unsupervised learning?

41. 

Which of the following is not a machine learning algorithm?

42. 

What is true about Machine Learning?

43. 

Which of the following is not machine learning?

44. 

Identify the method which is used for trainControl resampling.

45. 

Among the following option identify the one which is used to create the most common graph types.

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