What is naive Bayes classifier?

What is naive Bayes classifier?

A Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes’ theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be the ‘independent feature model’.

Can naive Bayes model be used in real-time predictions?

It can be used in real-time predictions because Naïve Bayes Classifier is an eager learner. It is used in Text classification such as Spam filtering and Sentiment analysis. There are three types of Naive Bayes Model, which are given below:

What does the kernel naive Bayes operator do?

This operator generates a Kernel Naive Bayes classification model using estimated kernel densities. A Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes’ theorem (from Bayesian statistics) with strong (naive) independence assumptions.

What is naivenaive Bayes in machine learning?

Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets. It can be used for Binary as well as Multi-class Classifications. It performs well in Multi-class predictions as compared to the other Algorithms. It is the most popular choice for text classification problems.

What is naive Bayes (kernel)?

The alternative Operator Naive Bayes (Kernel) is a variant of Naive Bayes where multiple Gaussians are combined, to create a kernel density. The input port expects an ExampleSet. The Naive Bayes classification model is delivered from this output port.

What are some use cases for naive Bayes?

Typical use cases involve text categorization, including spam detection, sentiment analysis, and recommender systems. The fundamental assumption of Naive Bayes is that, given the value of the label (the class), the value of any Attribute is independent of the value of any other Attribute.