Naïve Bayesian Classification Quiz

What is the main assumption of the Naïve Bayesian classifier?

• All features are correlated

• All features are independent given the class label

• All features are irrelevant

• All features are dependent on each other

Which of the following is NOT a type of Naïve Bayesian classifier?

• Linear Naïve Bayes

• Bernoulli Naïve Bayes

• Multinomial Naïve Bayes

• Gaussian Naïve Bayes

What is the purpose of the training phase in Naïve Bayesian classification?

• To evaluate the model performance

• To calculate the posterior probability

• To calculate the prior and likelihood probabilities

• To assign classes to new data points

In which application is Naïve Bayesian classification commonly used?

• Spam filtering

• Network security

• Image processing

• Data encryption

What does the term 'smoothing' refer to in Naïve Bayesian classification?

• Improving model interpretability

• Increasing the training dataset size

• Adjusting probabilities to handle zero counts

• Reducing the number of features

Which of the following is a disadvantage of Naïve Bayesian classifiers?

• Complex to implement

• Slow training and prediction

• Assumes feature independence

• Requires a large amount of training data

What is the first step in the prediction phase of Naïve Bayesian classification?

• Calculate the prior probability

• Calculate the likelihood probability

• Calculate the posterior probability for each class

• Assign the data point to the class

Which Naïve Bayesian classifier is suitable for binary features?

• Linear Naïve Bayes

• Multinomial Naïve Bayes

• Gaussian Naïve Bayes

• Bernoulli Naïve Bayes

What is the formula used to calculate the posterior probability in Naïve Bayesian classification?

• P(X)

• P(C)

• P(C|X)

• P(X|C)

In the context of Naïve Bayesian classifiers, what does 'P(C)' represent?

• Posterior probability

• Prior probability

• Likelihood probability

• Conditional probability