Naive Bayes Classifiers Quiz

Answers and explanations appear as you take the quiz.

1. What is the core 'naive' assumption in a Naive Bayes classifier?
   • Features are conditionally independent given the class
   • Classes are equally likely
   • All features are continuous and normally distributed
   • The dataset is perfectly balanced
2. Which theorem is Naive Bayes built on?
   • The central limit theorem
   • Bayes’ theorem
   • The law of large numbers
   • The Cauchy-Schwarz inequality
3. Why do implementations usually sum log-probabilities instead of multiplying raw probabilities?
   • To make the model nonlinear
   • To speed up training to a single pass
   • To avoid numeric underflow from multiplying many small numbers
   • To enforce feature independence
4. What problem does Laplace (add-one) smoothing solve?
   • It balances class sizes
   • It normalizes continuous features
   • It removes correlated features
   • It prevents an unseen feature from forcing a probability of zero
5. Which Naive Bayes variant is most natural for word-count features in documents?
   • Multinomial Naive Bayes
   • Gaussian Naive Bayes
   • Bernoulli Naive Bayes
   • Linear Naive Bayes
6. Why might Naive Bayes be useful as a lightweight baseline?
   • It guarantees the best accuracy on any dataset
   • It is fast, interpretable, and works well with little data
   • It automatically learns feature interactions
   • It requires no training data
7. What distribution does Gaussian Naive Bayes assume for each feature within a class?
   • A uniform distribution over every possible value
   • A multinomial distribution over word counts
   • A Gaussian (normal) distribution
   • A distribution that is always exactly zero
8. Which feature representation fits Bernoulli Naive Bayes most naturally?
   • The number of times each word occurs
   • A continuous pixel intensity modeled by a normal curve
   • A sequence of audio frames
   • Whether each word is present or absent in a document