Tutorials
PPTs
Lecture Notes
Objective Questions
Lab ProgramsObjective Type Questions & Answers
Machine Learning MCQs - Unit-1
1. Which of the following is not a type of machine learning?
A . Supervised Learning
B . Reinforcement Learning
C . Unsupervised Learning
D . Sequential Programming
2. In supervised learning, the training data consists of:
A . Input-output pairs
B . Only input features
C . Only outputs
D . Random samples
3. Which ML type is used for clustering problems?
A . Supervised Learning
B . Reinforcement Learning
C . Unsupervised Learning
D . Deep Learning
4. Reinforcement learning is based on:
A . Error correction
B . Rewards and punishments
C . Data clustering
D . Linear regression
5. In concept learning, the task is typically to infer:
A . A data structure
B . A target concept
C . A clustering algorithm
D . An unsupervised pattern
6. Candidate Elimination Algorithm maintains:
A . Training and test boundaries
B . Feature and target boundaries
C . Specific and general hypotheses
D . Input and output boundaries
7. Version space contains:
A . Only general hypotheses
B . All hypotheses consistent with training data
C . Only specific hypotheses
D . Inconsistent hypotheses
8. Decision tree is mainly used for:
A . Classification
B . Regression
C . Clustering
D . Dimensionality reduction
9. Linearly separable data can be classified using:
A . K-means
B . Perceptron
C . Naive Bayes
D . PCA
10. Concept learning aims to:
A . Find simplest hypothesis consistent with data
B . Memorize data
C . Build complex model
D . Reduce memory
11. A hypothesis refers to:
A . Mapping from input to output
B . Random guess
C . Neural weight
D . Dataset
12. Maximally specific hypothesis:
A . All generalizations
B . Ignores negatives
C . Covers only observed positives
D . Most general
13. Version space is:
A . All hypotheses
B . Consistent hypotheses
C . Minimum error hypotheses
D . General only
14. Candidate Elimination maintains:
A . Single hypothesis
B . Most general & most specific boundary
C . Random models
D . Regression equations
15. Recommendation systems mainly use:
A . Supervised Learning
B . Unsupervised Learning
C . Reinforcement Learning
D . Semi-supervised Learning
16. Legal move in checkers:
A . Forward
B . Backward
C . Side
D . Diagonal
17. _______ is supervised learning task:
A . Reinforcement
B . Dimensionality reduction
C . Clustering
D . Classification
18. Most general hypothesis:
A . ((Sunny, Warm, Strong, Humid)
B . ((Sunny, ?, ?, ?)
C . ((?,?,?,?)
D . ((φ, φ, φ, φ)
19. Multiple linear regression predicts:
A . Linear combination of inputs
B . Quadratic
C . Product
D . Squared sum
20. In Find-S, `?` denotes:
A . Specific
B . General
C . Null
D . Alternative
21. Perceptron purpose:
A . Clustering
B . Linear classification
C . Dimensionality reduction
D . Probability estimation
22. When misclassification occurs perceptron:
A . Adjusts weights
B . Stops
C . Resets
D . Ignores
23. Which is machine learning?
A . Supervised
B . Manual
C . Mechanical
D . Static
24. Supervised learning requires:
A . No output
B . Random data
C . Labeled data
D . Unlabeled
25. Neuron input part:
A . Axon
B . Dendrite
C . Synapse
D . Nucleus
26. Learning that improves with experience:
A . Traditional
B . Machine learning
C . Manual
D . Static
27. Concept learning used for:
A . Regression
B . Classification
C . Clustering
D . Sorting
28. Concept learning is:
A . Optimization
B . Search problem
C . Graph
D . Sorting
29. Most general hypothesis symbol:
A . Ø
B . ?
C . NULL
D . MAX
30. Version space contains:
A . All hypotheses
B . Consistent hypotheses
C . Random
D . None
31. Candidate Elimination proposed by:
A . McCarthy
B . Tom Mitchell
C . Rosenblatt
D . Hebb
32. Perceptron uses:
A . Non-linear
B . Step function
C . Gaussian
D . Polynomial
33. Perceptron classifies:
A . Non-linear
B . Random
C . Linear
D . Clustered
34. Linear separability means separation by:
A . Curve
B . Circle
C . Straight line
D . Hyperbola
35. Linear regression is used for:
A . Classification
B . Prediction
C . Clustering
D . Association
36. Linear regression minimizes:
A . Absolute error
B . Squared error
C . Classification error
D . Entropy
37. Perceptron output is:
A . Continuous
B . Probabilistic
C . Binary
D . Multiclass
Fill in the Blanks
38. Supervised learning uses ______________ data.
39.Most specific hypothesis = _________________
40. Concept learning searches ___________________space.
41. Find-S ignores _______________examples.
42. Version space contains ________________hypotheses.
43. Classification predicts _______________values.
44. Machine learning learns from__________________.
45. Unsupervised learning uses _______________ data.
46. Basic brain unit is____________________.
47. Dendrites receive __________________ signals.
48. Axon transmits ________________ signals.
49. Learning system includes ________________ element.
50. Boolean function is learned in _______________________
51. Most general hypothesis is ____________
52. Candidate Elimination maintains _________________ boundaries.
53. Perceptron is ________________ classifier.
54. Linear regression predicts _______________ values.
55. Overfitting means model _______________ training data.
56. Good performance on unseen data is _______________.
57. ID3 uses____________________________.
58. Target concept is predicted in________________.
59. Hypothesis consistent with all data is ________________ hypothesis.
60. Straight line separates _________________ data.
61. Learning improves with _________________________.
62. Search problem formulation is used in_________________.
63. Most specific symbol is_________________.
64. Most general symbol is_______________.
65. Regression predicts __________________ values.
66. Hypothesis space is searched in_________________________.
67. Candidate Elimination refines______________________.
Relevant Materials :
☞ Machine Learning MCQs - Unit-1 - [ ML ]
☞ Machine Learning MCQs - Unit-2 - [ ML ]
Similar Materials :
☞ R - Programming MCQs - Unit-1 - [ R-Programming ]
☞ R - Programming MCQs - Unit-2 - [ R-Programming ]
☞ R - Programming MCQs - Unit-3 - [ R-Programming ]
☞ R - Programming MCQs - Unit-4 - [ R-Programming ]
☞ R - Programming MCQs - Unit-5 - [ R-Programming ]
☞ Data Mining MCQs - Unit-1 - [ DM ]
☞ Data Mining MCQs - Unit-2 - [ DM ]
☞ Data Mining MCQs - Unit-3 - [ DM ]
☞ Data Mining MCQs - Unit-4 - [ DM ]
☞ Data Mining MCQs - Unit-5 - [ DM ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-1 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-2 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-3 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-4 - [ FLAT ]
☞ Formal Languages and Automata Theory (FLAT) MCQs - Unit-5 - [ FLAT ]
☞ Artificial Intelligence (AI) MCQs - Unit-1 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-2 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-3 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-4 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-5 - [ Artificial Intelligence ]
☞ Design and Analysis of Algorithms MCQs - Unit-1 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-2 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-3 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-4 - [ DAA ]
☞ Design and Analysis of Algorithms MCQs - Unit-5 - [ DAA ]
☞ Object Oriented Programming through Java MCQs - Unit-1 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-2 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-3 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-4 - [ OOP_JAVA ]
☞ Object Oriented Programming through Java MCQs - Unit-5 - [ OOP_JAVA ]
☞ Database Management System Objective Type Question Bank-Unit-1 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-2 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-3 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-4 - [ DBMS ]
☞ Database Management System Objective Type Question Bank-Unit-5 - [ DBMS ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-1 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-2 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-3 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-4 - [ COA ]
☞ Computer Organization and Architecture (COA) Objective Question Bank-Unit-5 - [ COA ]
☞ Software Engineering MCQs - Unit-1 - [ SE ]
☞ Software Engineering MCQs - Unit-2 - [ SE ]
☞ Software Engineering MCQs - Unit-3 - [ SE ]
☞ Software Engineering MCQs - Unit-4 - [ SE ]
☞ Software Engineering MCQs - Unit-5 - [ SE ]
☞ Operating Systems -Unit-1 Objective Type Questions - [ Operating Systems ]
☞ Operating Systems -Unit-2 Objective Type Questions - [ Operating Systems ]
☞ Operating Systems -Unit-3 Objective Type Questions - [ Operating Systems ]
☞ Operating Systems -Unit-4 Objective Type Questions - [ Operating Systems ]
☞ Operating Systems -Unit-5 Objective Type Questions - [ Operating Systems ]