MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task. The crux of the classifier is based on the Bayes theorem.

Detailed explanation-2: -The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions. The independence assumptions often do not have an impact on reality. Therefore they are considered as naive.

Detailed explanation-3: -Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.

Detailed explanation-4: -What Is the Naive Bayes Algorithm? It is a classification technique based on Bayes’ Theorem with an independence assumption among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Detailed explanation-5: -Advantages of Naive Bayes Classifier It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points. It is fast and can be used to make real-time predictions. It is not sensitive to irrelevant features.