MCQs

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Overall MCQs for Reference

1. Which assumption does the Naive Bayes algorithm make regarding features?
   a. Conditionally dependent
   b. Conditionally independent
   c. Mutually exclusive
   d. Correlated
   Answer: b. Conditionally independentsanfoundry+2​
2. Naive Bayes is based on which mathematical theorem?
   a. Bayes’ Theorem
   b. Newton’s Law
   c. Pythagoras’ Theorem
   d. Markov’s Principle
   Answer: a. Bayes’ Theoremtutorialspoint+1​
3. Naive Bayes is mainly used in which ML task?
   a. Regression
   b. Clustering
   c. Classification
   d. Association
   Answer: c. Classificationupgrad+1​
4. Which of the following fields uses Naive Bayes for classification?
   a. Text classification
   b. Image segmentation
   c. Time series forecasting
   d. Data mining
   Answer: a. Text classificationsanfoundry+1​
5. Which type of model is Naive Bayes?
   a. Generative
   b. Discriminative
   c. Ensemble
   d. Parametric
   Answer: a. Generativegeeksforgeeks+1​
6. The “naive” assumption in Naive Bayes is:
   a. Features are independent given the class
   b. Classes are dependent
   c. Only binary features are considered
   d. Parameters are fixed
   Answer: a. Features are independent given the classscikit-learn+2​
7. Which of these is Bayes’ theorem’s correct formula for classification?
   a. P(A|B) = P(B|A)P(A)/P(B)
   b. P(A|B) = P(A) + P(B)
   c. P(A|B) = P(B)/P(A)
   d. P(A|B) = P(A)-P(B)
   Answer: a. P(A|B) = P(B|A)P(A)/P(B) sanfoundry+1​
8. Which of the following is a limitation of Naive Bayes?
   a. The independence assumption rarely holds
   b. High computational cost
   c. Cannot handle missing data
   d. Works only for binary classification
   Answer: a. The independence assumption rarely holdsdatascientest+2​
9. Which situation leads to the “zero-frequency problem” in Naive Bayes?
   a. An unseen categorical variable appears in test data
   b. Too much training data
   c. Continuous features only
   d. Incomplete classes
   Answer: a. An unseen categorical variable appears in test datakeylabs+1​
10. What method is often used to deal with zero-frequency problem?
    a. Regularization
    b. Laplace smoothing
    c. Feature scaling
    d. Principal component analysis
    Answer: b. Laplace smoothingupgrad+1​
11. Naive Bayes can perform well with:
    a. Large datasets only
    b. Small datasets only
    c. Both large and small datasets
    d. Only imbalanced datasets
    Answer: c. Both large and small datasetskeylabs+1​
12. Which is generally NOT required for Naive Bayes algorithm?
    a. Categorical features
    b. Labelled data
    c. Neural networks
    d. Independence among features
    Answer: c. Neural networkscsethings+1​
13. Which variant of Naive Bayes is best for continuous data?
    a. Gaussian Naive Bayes
    b. Multinomial Naive Bayes
    c. Bernoulli Naive Bayes
    d. Polynomial Naive Bayes
    Answer: a. Gaussian Naive Bayesdevinterview+1​
14. Spam filtering is a typical application for:
    a. Naive Bayes
    b. Linear regression
    c. Decision tree
    d. SVM
    Answer: a. Naive Bayestutorialspoint+1​
15. “Conditional Independence” in Naive Bayes refers to:
    a. Features are independent given the class label
    b. Class labels are independent
    c. Features and labels are independent
    d. Classes are dependent
    Answer: a. Features are independent given the class labelaimcqs+1​
16. Naive Bayes can be used for:
    a. Binary classification
    b. Multi-class classification
    c. Both a and b
    d. Regression
    Answer: c. Both a and btutorialspoint+1​
17. In Naive Bayes, the prior probability is:
    a. The probability of each class
    b. The probability of each feature
    c. The likelihood of data given class
    d. The evidence probability
    Answer: a. The probability of each classscikit-learn+1​
18. Which variant of Naive Bayes is suited for text data?
    a. Multinomial Naive Bayes
    b. Gaussian Naive Bayes
    c. Polynomial Naive Bayes
    d. Discrete Naive Bayes
    Answer: a. Multinomial Naive Bayesdevinterview+1​
19. Naive Bayes Classifier predicts the class with:
    a. Lowest probability
    b. Highest posterior probability
    c. Highest prior probability
    d. Random chance
    Answer: b. Highest posterior probabilityscikit-learn+1​
20. Which can negatively impact Naive Bayes performance?
    a. Correlated features
    b. Large dataset size
    c. Laplace smoothing
    d. Categorical variables
    Answer: a. Correlated featureskaggle+2​
21. When using Naive Bayes, numerical variables usually:
    a. Are binned and converted to categorical values
    b. Are ignored
    c. Are forced into binary values
    d. Must be squared
    Answer: a. Are binned and converted to categorical valuescsethings+1​
22. Which of the following is NOT a benefit of Naive Bayes?
    a. Fast training
    b. Handles continuous and discrete data
    c. High model complexity
    d. Highly scalable
    Answer: c. High model complexityupgrad+1​
23. Sentiment analysis and recommendation systems can use:
    a. Naive Bayes classifiers
    b. Clustering algorithms only
    c. Neural networks only
    d. PCA algorithms
    Answer: a. Naive Bayes classifierstutorialspoint+1​
24. Which of these is an output of Naive Bayes classifier?
    a. Probabilities for each class
    b. Regression coefficient
    c. Cluster centroids
    d. Decision boundary
    Answer: a. Probabilities for each classgeeksforgeeks+2​
25. In Naive Bayes, P(class|features) is calculated by multiplying:
    a. Prior and likelihood
    b. Prior and evidence
    c. Prior and regression slope
    d. Feature values only
    Answer: a. Prior and likelihoodscikit-learn+1​
26. Which is a disadvantage of Naive Bayes?
    a. Poor generalization with unseen events
    b. Requires neural network structure
    c. Works only for continuous data
    d. Needs too much data
    Answer: a. Poor generalization with unseen eventskeylabs+2​
27. A key advantage of Naive Bayes over other ML classifiers:
    a. Very few parameters to estimate
    b. Requires feature engineering
    c. Only works with image data
    d. High memory usage
    Answer: a. Very few parameters to estimategeeksforgeeks+1​
28. Which form of Naive Bayes is appropriate for binary features?
    a. Bernoulli Naive Bayes
    b. Multinomial Naive Bayes
    c. Gaussian Naive Bayes
    d. Normal Naive Bayes
    Answer: a. Bernoulli Naive Bayesdevinterview+1​
29. Naive Bayes can be used in which cybersecurity task?
    a. Spam filtering
    b. Key exchange
    c. DDoS attack simulation
    d. Firewall management
    Answer: a. Spam filteringcore+1​
30. The evidence probability in Bayes’ theorem is:
    a. Probability of features given all classes
    b. Probability of the class given feature
    c. Prior probability of features
    d. Likelihood of class
    Answer: a. Probability of features given all classestutorialspoint+1​
31. Naive Bayes is less effective when:
    a. Predictors are dependent
    b. Data is high dimensional
    c. Data is numeric
    d. Dataset is small
    Answer: a. Predictors are dependentdatascientest+2​
32. How does Naive Bayes treat missing features?
    a. Ignores them
    b. Treats as zero probability
    c. Applies smoothing
    d. Discards the instance
    Answer: c. Applies smoothingwayground+1​
33. Which statement is TRUE about Naive Bayes?
    a. It assigns probabilities based on training data frequencies
    b. It requires deep learning networks
    c. It ignores probabilities
    d. It uses only supervised learning methods
    Answer: a. Assigns probabilities based on training data frequenciesscikit-learn+1​
34. In Naive Bayes, the likelihood is:
    a. P(feature|class)
    b. P(class|feature)
    c. P(feature|feature)
    d. P(class|class)
    Answer: a. P(feature|class) scikit-learn+1​
35. Which algorithm does NOT use Bayes’ theorem?
    a. Decision Trees
    b. Gaussian Naive Bayes
    c. Multinomial Naive Bayes
    d. Bernoulli Naive Bayes
    Answer: a. Decision Treesgeeksforgeeks+1​
36. Naive Bayes assumes data is:
    a. Conditionally independent
    b. Correlated
    c. Nested
    d. Linear
    Answer: a. Conditionally independentaimcqs+2​
37. In terms of scalability, Naive Bayes:
    a. Scales linearly with the number of predictors and data points
    b. Scales exponentially with dataset size
    c. Has quadratic complexity
    d. Does not scale
    Answer: a. Scales linearly with predictors and data pointsupgrad+1​
38. Which ML problem is Naive Bayes NOT suitable for?
    a. Image classification with highly correlated pixels
    b. Text classification
    c. Sentiment analysis
    d. Binary classification
    Answer: a. Image classification with highly correlated pixelsdatascientest+1​
39. Recommendation systems can benefit from:
    a. Naive Bayes along with collaborative filtering
    b. Only clustering algorithms
    c. Only regression analysis
    d. SVM models only
    Answer: a. Naive Bayes and collaborative filteringupgrad+1​
40. “Posterior Probability” in Bayes’ theorem means:
    a. Probability of class given features
    b. Probability of feature given class
    c. Likelihood of class occurring
    d. Prior probability of feature
    Answer: a. Probability of class given featuressanfoundry+2​
41. Which of the following is a multi-class problem suitable for Naive Bayes?
    a. News article classification
    b. Fraud detection
    c. Predicting house prices
    d. Linear regression tasks
    Answer: a. News article classificationtutorialspoint+1​
42. In Naive Bayes, the classifier fails when:
    a. Features are strongly dependent
    b. Dataset is imbalanced
    c. Training data is sufficient
    d. Data is standardized
    Answer: a. Features are strongly dependentkaggle+2​
43. What is a key application area for Naive Bayes in industry?
    a. Spam filtering and text classification
    b. Computer vision
    c. Cryptography
    d. DRAM design
    Answer: a. Spam filtering and text classificationsanfoundry+2​
44. Which smoothing technique is most common in Naive Bayes?
    a. Laplace smoothing
    b. LASSO regression
    c. Tikhonov regularization
    d. Min-max scaling
    Answer: a. Laplace smoothingkeylabs+1​
45. What type of learning is used in Naive Bayes?
    a. Supervised learning
    b. Reinforcement learning
    c. Unsupervised learning
    d. Semi-supervised learning
    Answer: a. Supervised learninggeeksforgeeks+1​
46. Which is NOT a disadvantage of Naive Bayes?
    a. Requires extensive training data
    b. Assumes feature independence
    c. Influenced by irrelevant attributes
    d. Zero probability for unseen events
    Answer: a. Requires extensive training dataupgrad+2​
47. Naive Bayes can provide:
    a. Probabilistic predictions
    b. Deterministic predictions only
    c. No predictions
    d. Regression coefficients
    Answer: a. Probabilistic predictionsscikit-learn+2​
48. What is the primary assumption of Multinomial Naive Bayes?
    a. Features are drawn from a multinomial distribution
    b. Features are Gaussian
    c. Features are continuous
    d. Features are binary
    Answer: a. Features are drawn from a multinomial distributiondevinterview+1​
49. Which step is NOT required in Naive Bayes classification?
    a. Estimate prior probabilities
    b. Estimate likelihoods
    c. Estimate posterior probabilities
    d. Normalize feature values to zero mean
    Answer: d. Normalize feature values to zero meantutorialspoint+1​
50. Naive Bayes model performs well for:
    a. High dimensional data with many features
    b. Datasets with highly correlated features
    c. Only numeric data
    d. Very small datasets
    Answer: a. High dimensional data with many featureskeylabs+1​

1. https://www.sanfoundry.com/machine-learning-questions-answers-naive-bayes-algorithm/
2. https://aimcqs.com/bayesian
3. https://scikit-learn.org/stable/modules/naive_bayes.html
4. https://www.tutorialspoint.com/machine_learning/machine_learning_naive_bayes_algorithms.htm
5. https://testbook.com/objective-questions/mcq-on-bayess-theorem–5eea6a0a39140f30f369dc5d
6. https://www.upgrad.com/blog/naive-bayes-explained/
7. https://www.geeksforgeeks.org/machine-learning/naive-bayes-classifiers/
8. https://www.ibm.com/think/topics/naive-bayes
9. https://datascientest.com/en/all-about-naive-bayes-classifier
10. https://www.kaggle.com/getting-started/225022
11. https://keylabs.ai/blog/naive-bayes-classifiers-types-and-use-cases/
12. https://csethings.com/naive-bayes-mcqs/
13. https://devinterview.io/blog/naive-bayes-interview-questions
14. https://core.ac.uk/download/pdf/334411238.pdf
15. https://wayground.com/admin/quiz/60a72b5e8a112e001bdd6d1d/naive-bayes
16. https://pages.cs.wisc.edu/~sharonli/courses/cs540_spring2021/documents/perceptron-quiz.pdf
17. https://www.geeksforgeeks.org/quizzes/naive-bayes-algorithm-with-implementation/
18. https://www.scribd.com/document/690484163/UNIT-III-ML-MCQ
19. https://www.youtube.com/watch?v=4KGdpM0R_qU
20. https://www.youtube.com/watch?v=O2L2Uv9pdDA