[amp_mcq option1=”To maximize the margin between classes” option2=”To minimize prediction errors” option3=”To classify data points based on features” option4=”To perform unsupervised learning” correct=”option3″]
The correct answer is C. To classify data points based on features.
A naive Bayes classifier is a probabilistic machine learning model that uses Bayes’ theorem to classify data points. It is a simple and effective model that is often used for text classification, spam filtering, and other tasks where the data can be easily represented as a set of features.
The naive Bayes classifier assumes that the features are independent of each other, which is often not the case in real-world data. However, the model can still be effective even if this assumption is not strictly true.
The naive Bayes classifier is a powerful tool that can be used to classify data points with a high degree of accuracy. It is a simple and easy-to-use model that is often used in a variety of applications.
Here is a brief explanation of each option:
- A. To maximize the margin between classes: This is the goal of a support vector machine (SVM), which is a different type of machine learning model.
- B. To minimize prediction errors: This is the goal of many machine learning models, but it is not the specific goal of a naive Bayes classifier.
- D. To perform unsupervised learning: This is a type of machine learning where the model is not given any labels for the data points. A naive Bayes classifier is a supervised learning model, which means that it is given labels for the data points.