Virtual Labs

a: Discretize the features into discrete intervals b: Use Gaussian probability distribution for each feature c: Convert the features into binary values d: Ignore the continuous features and focus on categorical features

How does Laplace smoothing (additive smoothing) help address the zero probability problem in Naive Bayes?

a: It assigns a small non-zero probability to unseen events b: It assigns a zero probability to unseen events c: It increases the count of each event by 1 d: It reduces the count of each event by 1

When should you consider using Multinomial Naive Bayes over Gaussian Naive Bayes?

a: When dealing with binary classification problems b: When the dataset contains continuous numerical features c: When the dataset contains categorical features d: When the dataset has imbalanced class distributions

What is the purpose of feature selection in Naive Bayes?

a: To reduce the dimensionality of the feature space b: To remove features that are not statistically significant c: To improve the performance of the Naive Bayes classifier d: To handle missing values in the dataset

When is it appropriate to use Naive Bayes in real-world applications?

a: When the dataset has a large number of features and a small number of instances b: When the class conditional independence assumption is violated c: When the dataset contains a mixture of continuous and categorical features d: When the class labels are highly correlated with each other

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