Introduction to Gaussian Naive Bayes Answers 1. What is a significant limitation of Gaussian Naive Bayes? A. It can handle complex relationships effectively. B. It requires extensive hyperparameter tuning. C. It is highly sensitive to irrelevant features. D. It performs poorly in medical diagnoses. Answer: It is highly sensitive to irrelevant features. (C) Gaussian Naive Bayes is sensitive to irrelevant features, which can negatively impact its performance. 2. Which application is commonly associated with Gaussian Naive Bayes? A. Image recognition B. Spam filtering C. Time series forecasting D. Neural network training Answer: Spam filtering (B) Gaussian Naive Bayes is widely used for spam filtering, among other applications. 3. Which of the following metrics is NOT commonly used to evaluate the performance of a model? A. Accuracy B. F1-score C. Precision D. Survival rate Answer: Survival rate (D) Survival rate is not a standard metric used for evaluating model performance like accuracy, precision, and F1score. 4. Why is Gaussian Naive Bayes often considered easier to use compared to other classification algorithms? A. It usually requires extensive data preprocessing. B. It has a large number of tunable parameters. C. It is the most complex algorithm available. D. It typically has fewer tuning parameters. Answer: It typically has fewer tuning parameters. (D) Gaussian Naive Bayes is considered ready-to-use due to having fewer tuning parameters compared to other classification algorithms. 5. What aspect should be considered when choosing an evaluation metric for a model? A. The importance of different types of errors B. The size of the dataset C. The speed of the learning process D. The algorithm's complexity Answer: The importance of different types of errors (A) The choice of evaluation metric should depend on the specific application and the importance of different types of errors. 6. What fundamental statistical principle does Gaussian Naive Bayes rely on to calculate posterior probabilities? A. Markov Chain B. Frequentist statistics C. Bayes' theorem D. Central Limit Theorem Answer: Bayes' theorem (C) Bayes' theorem is crucial for calculating how likely a class is given the observed data. 7. Which assumption is made by the Gaussian Naive Bayes algorithm regarding the features? A. Features are mutually exclusive. B. Features are unrelated to the class label. C. Features are conditionally dependent on the class label. D. Features are conditionally independent given the class label. Answer: Features are conditionally independent given the class label. (D) The assumption of conditional independence simplifies the calculation of probabilities. 8. In Gaussian Naive Bayes, how are the feature distributions for each class typically modeled? A. Exponential distribution B. Uniform distribution C. Gaussian (normal) distribution D. Logarithmic distribution Answer: Gaussian (normal) distribution (C) Gaussian distribution is used due to its properties in handling continuous features. 9. What is one of the main advantages of using Gaussian Naive Bayes in machine learning? A. Requires extensive hyperparameter tuning. B. High complexity allowing for detailed modeling. C. Computationally inexpensive with high dimensional features. D. Ability to handle random forest algorithms. Answer: Computationally inexpensive with high dimensional features. (C) Its computational efficiency makes it practical for large datasets with many features. 10. What does the term 'prior probability' refer to in the context of Gaussian Naive Bayes? A. The probability of predicting a class without any data. B. The probability assigned to each feature independently. C. The probability calculated after observing the data. D. The probability of a class based solely on historical data. Answer: The probability of a class based solely on historical data. (D) Prior probability is based on the distribution of classes in the training dataset. 11. What happens to the accuracy of Gaussian Naive Bayes when features are correlated? A. Accuracy is not affected by feature correlation. B. Accuracy improves with correlation. C. Accuracy decreases drastically and becomes unusable. D. Accuracy can decline due to the independence assumption. Answer: Accuracy can decline due to the independence assumption. (D) Correlation among features violates the independence assumption, impacting performance. 12. Which of the following is a limitation of Gaussian Naive Bayes? A. It performs poorly with high-dimensional data. B. It assumes all features follow a Gaussian distribution. C. It is only applicable to categorical data. D. It can only make binary classifications. Answer: It assumes all features follow a Gaussian distribution. (B) The assumption that features follow a Gaussian distribution can limit its effectiveness. 13. Which step in Gaussian Naive Bayes involves calculating the probability density for each feature? A. Estimating Bayesian factors B. Computing likelihood of data points C. Applying posterior probability D. Calculating prior probabilities Answer: Computing likelihood of data points (B) The likelihood is crucial for determining how well a feature value aligns with a class.