Naive Bayes:

Simple and effective common classification algorithm , Typical use ： Spam classification

hypothesis ： Given the target value, the attributes are conditionally independent of each other Again , The Bayesian estimate of a priori probability is 1、 One kind of unsupervised learning , Implement a simple , No iterations , High learning efficiency , It will have better performance in large sample size .

2、 There is a relatively simple explanation for the learning of classifiers , The classification principle can be understood simply through some probability values calculated during query learning .

shortcoming ：

1、 The assumption is too strong —— Suppose that the characteristic conditions are independent , It is not applicable in the scene where the characteristic conditions of the input vector are related .

#################################Weka############################### ######################R Language ##################################

##########klaR In bag NaiveBayes function , Because this function adds two functions to the former , One is that you can input a priori probability , The other is to add the kernel smooth density function to the normal distribution ###################

library(klaR)

data(iris)

mN <- NaiveBayes(Species ~ ., data = iris)

plot(mN)

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