Naive Bayes classifier

Naive Bayes Classifiers are a type of machine learning algorithm. Machine learning algorithms are special types of computer programs that can learn from data and make predictions.

Using Naive Bayes Classifiers, a computer looks at data (like pieces of text, numbers, or images) and tries to figure out which category it belongs to. For example, if the computer has seen lots of pictures of cats, it can become good at recognizing cats in new pictures and telling them apart from other types of animals.

The "Naive" part of Naive Bayes Classifiers means that the computer makes a few simplifying assumptions when learning from data. In other words, it assumes that each piece of data is completely independent from all the others. This helps the computer learn faster and saves it from getting confused.