Naive Bayes (Naive) explain the meaning of

"Naive" Origin

Naive Bayes (Naive Bayes) is a group of supervised learning algorithm, which is based on Bayes' theorem, "naively" "naive" is assumed independent of each other between the features, i.e., the presence of a characteristic independent of the presence of other features. This is the "simple" reason the word "Naive".
Bayes's law and case: https://blog.csdn.net/houhuipeng/article/details/90706539

sample $E=(x_1,x_2...x_n)$ Belong to the category $c$ probability:
$p(c|E)=\frac{p(E|c)p(c)}{p{E}}$When satisfied $E$ is $c$ 's $﹢$ Probability greater than Example $-$ When the embodiment, may be determined $E$ belong $c$
$f_b(E)=\frac {p(C=+|E)}{p=-|E} \geq1$ is assumed mutually independent features, then $p(E|c)=p(x_1,x_2...x_n|c)=\prod_{i=1}^np(x_i|c)$
finally available: $f_{nb}(E)=\frac{p(C=+)}{p(C=-)}\prod_{i=1}^n\frac{p(x_i|C=+)}{p(x_i|C=-)}$

Advantages and disadvantages

Naive Bayes model is a set of extremely quick and simple classification algorithm, usually for very high-dimensional data sets. Because they are so fast and tunable small, so they end up as a very useful basis for rapid classification problems.
Advantages: speed operation, less simple model tunable parameters.
Cons: assumption is not true in most cases.
Depending on the assumptions feature distribution can be divided into:

• Naive Bayesian Gaussian
• Naive Bayes polynomial
• Supplementary Naive Bayes
• Bernoulli Naive Bayes

Participate sklearn official document:
https://scikit-learn.org/stable/modules/naive_bayes.html