Machine Learning MCQs - Basics & Supervised Learning

Machine Learning MCQs

machine Learning mcqs

Basics & Supervised Learning (1-100)

1. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

2. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

3. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

4. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

5. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

6. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

7. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

8. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

9. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

10. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

11. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

12. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

13. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

14. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

15. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

16. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

17. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

18. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

19. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

20. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

21. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

22. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

23. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

24. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

25. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

26. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

27. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

28. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

29. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

30. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

31. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

32. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

33. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

34. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

35. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

36. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

37. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

38. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

39. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

40. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

41. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

42. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

43. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

44. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

45. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

46. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

47. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

48. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

49. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

50. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

51. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

52. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

53. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

54. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

55. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

56. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

57. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

58. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

59. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

60. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

61. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

62. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

63. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

64. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

65. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

66. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

67. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

68. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

69. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

70. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

71. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

72. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

73. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

74. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

75. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

76. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

77. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

78. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

79. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

80. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

81. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

82. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

83. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

84. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

85. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

86. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

87. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

88. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

89. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

90. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.

91. What is the main goal of supervised learning?

• A. To group data without labels
• B. To discover hidden patterns
• C. To learn a mapping from inputs to known outputs
• D. To minimize entropy

Answer: C

Supervised learning uses labeled data to train models to predict outcomes based on input.

92. Which of the following is a supervised learning algorithm?

• A. K-means
• B. Linear Regression
• C. PCA
• D. DBSCAN

Answer: B

Linear Regression is a supervised learning algorithm that models the relationship between inputs and outputs.

93. What is the difference between classification and regression?

• A. Classification predicts continuous output, regression predicts categories
• B. Classification predicts categories, regression predicts continuous output
• C. Both predict categories
• D. Both predict continuous output

Answer: B

Classification predicts discrete labels, while regression predicts continuous values.

94. Which of these is NOT a supervised learning algorithm?

• A. Decision Trees
• B. Support Vector Machines
• C. K-Means Clustering
• D. Linear Regression

Answer: C

K-Means is an unsupervised learning algorithm used for clustering.

95. What is the purpose of training data in supervised learning?

• A. To test the model's performance
• B. To tune hyperparameters only
• C. To teach the model how to map inputs to outputs
• D. To evaluate the final model

Answer: C

Training data is used to train the model to learn the relationship between inputs and outputs.

96. What does overfitting mean?

• A. Model performs poorly on training data
• B. Model performs well on unseen data
• C. Model memorizes training data and performs poorly on test data
• D. Model is too simple

Answer: C

Overfitting happens when a model learns noise and details from training data, hurting its ability to generalize.

97. What is cross-validation used for?

• A. Increasing training data size
• B. Assessing model’s generalization on unseen data
• C. Visualizing data distribution
• D. Reducing dimensionality

Answer: B

Cross-validation helps estimate how well a model generalizes by testing it on multiple subsets of data.

98. Which loss function is commonly used for regression tasks?

• A. Mean Squared Error
• B. Cross Entropy
• C. Hinge Loss
• D. KL Divergence

Answer: A

Mean Squared Error measures the average squared difference between estimated values and actual values.

99. What technique is used to prevent overfitting?

• A. Regularization
• B. Increasing model complexity
• C. Ignoring validation data
• D. Using small training sets

Answer: A

Regularization adds a penalty to the loss function to discourage complex models.

100. Which activation function is commonly used in neural networks?

• A. ReLU
• B. Sigmoid
• C. Tanh
• D. All of the above

Answer: D

ReLU, Sigmoid, and Tanh are all common activation functions used in neural networks.