Naive Bayes classifiers are a collection . . . . . . . . of algorithms

The correct answer is: A. Classification.

Naive Bayes classifiers are a collection of supervised learning algorithms that are used for classification. They are based on Bayes’ theorem, which is a mathematical formula that is used to calculate the probability of an event occurring given the occurrence of other events.

Naive Bayes classifiers are simple to understand and implement, and they are often used in spam filtering, web search engines, and natural language processing.

Here is a brief explanation of each option:

• Option A: Classification. Naive Bayes classifiers are a collection of supervised learning algorithms that are used for classification.
• Option B: Clustering. Clustering is a type of unsupervised learning algorithm that is used to group data points together.
• Option C: Regression. Regression is a type of supervised learning algorithm that is used to predict continuous values.
• Option D: All. Naive Bayes classifiers are not a collection of all of these algorithms. They are a collection of supervised learning algorithms that are used for classification.