2016年9月11日 · The Naive Bayes classifier approximates the Optimal Bayes classifier by looking at the empirical distribution and by assuming conditional independence of explanatory variables, given a class. So the Naive Bayes classifier is not itself optimal, but it …

2011年11月10日 · A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. Bayes' theorem was named after the Reverend Thomas Bayes (1702–61), who studied how to compute a distribution for the probability parameter of a binomial distribution.

2018年12月21日 · To say that a naive Bayes classifier is the Bayes classifier would just mean that no classifier can perform better in terms of misclassification rate (that is, it "knows" all the marginal distributions of the predictors for each class and correctly assumes they're all independent).

2021年9月24日 · Gaussian Naive Bayes classifier is a special case of LDA. If you consider the naive Bayes classifier with the assumption of Gaussian distributions and the same variance for different groups/classes, then you could see this as a special case of LDA. It is like LDA with the restriction that the covariance matrix $\Sigma$ is diagonal. Other point ...

2015年3月18日 · What I have continually read is that Naive Bayes is a linear classifier (ex: here) (such that it draws a linear decision boundary) using the log odds demonstration. However, I simulated two Gaussian clouds and fitted a decision boundary and got the results as such (library e1071 in r, using naiveBayes())

2017年8月3日 · Naive Bayes classifier approximates the optimal classifier by looking at the empirical distribution and by assuming independence of predictors. So naive Bayes classifier is not itself optimal, but it approximates the optimal solution. In your question you seem to confuse those two things.

We can combine the two and add some connections between the features of the Naive Bayes and it becomes the tree augmented Naive Bayes or k-dependence Bayesian classifier. References: 1. Bayesian Network Classifiers

2018年1月17日 · TLDR: naive Bayes algorithm uses empirical probabilities (observed fractions) and then classifies by choosing the class that has greatest a posteriori probability (that's the argmax part). As simple as that. $\endgroup$

The distribution you had been using with your Naive Bayes classifier is a Guassian p.d.f., so I guess you could call it a Guassian Naive Bayes classifier. In summary, Naive Bayes classifier is a general term which refers to conditional independence of each of the features in the model, while Multinomial Naive Bayes classifier is a specific ...

2019年5月3日 · But not always. Naive Bayes, although very simple, it performs very well on complex problems with complex data sets. The only thing that Naive Bayes assumes is that all features/variables are independent, and even this is a loose restriction. This is an investigation that you will have to take on for your own specific problem.