What are Bayesian classifiers?
Bayesian classifiers are statistically based classifiers which can predict the class
label probabilities that the data belongs in that label. It is based on Bayes' theorem
and these algorithms are comparable in performance with decision trees and neural network classifiers. They have high accuracy and speed on large data sets.
How does the naïve Bayesian classifier work?
The simple bayesian classifier works by representing each data point as a set of vectors. If there are m classes, given the the vector the classifier will predict that it belongs to label m_i. We try to predict the probability for every class label and then choosing the highest probability.
How effective are Bayesian classifiers?
Like mentioned earlier bayesian classifiers are comparable to decision trees and neural networks. This varies by domain but in theory bayesian classifiers have the least error rate compared to all other classification approaches. This can all of course vary depending on the quality of the data set given.
What is rule-based classification?
Rule based classification is a classifier represented by IF-THEN statements which is a good way to represent information or bits of knowledge. An example of such rule goes as follows:
R1:IF age = youth AND student = yes THEN buys_computer = yes.
The if side of the rule is called the rule antecedent also known as the precondition. The then side is the rule consequent.