Naive Bayes classifier is a type of supervised learning - True or False?Answer choicesSelect an optionTRUEFALSEUnsupervised learningNone of the options

Answer:

The statement "Naive Bayes classifier is a type of supervised learning" is True.

Explanation:

1. Definition of Naive Bayes Classifier: The Naive Bayes classifier is a statistical classification technique based on Bayes' Theorem. It is called "naive" because it assumes that the features used for classification are independent of one another, which simplifies the calculations involved.

2. Nature of Supervised Learning: Supervised learning involves training a model on a labeled dataset, meaning that the training data includes both the input features and the corresponding target outputs (labels). In the case of Naive Bayes, the algorithm leverages the labeled training data to learn the probabilities associated with different classes based on the features.

3. Implementation of Naive Bayes:

   • During training, the Naive Bayes algorithm calculates prior probabilities for each class and the likelihood of each feature given the class.
   • When making predictions, it applies Bayes' Theorem to determine the class with the highest posterior probability, utilizing the previously calculated data.

4. Conclusion: Given that Naive Bayes relies on labeled training data for its predictions, it is indeed classified as a supervised learning method. Thus, the correct choice is TRUE.

Final Answer

TRUE