Naive Bayes Assumption

In naive Bayes, one assumes that

$$P({\text{Cause, Effect}}_1,\dots ,{\text{Effect}}_n)=P(\text{Cause})\prod _{i}P({\text{Effect}}_i|\text{Cause})$$

(conditional independence is an example of naive Bayes)

Total number of parameters is linear in the number of conditionally independent effects $n$. We call this ‘naive’ because it is oversimplifying as in many cases the ‘effect’ variables aren’t actually conditionally independent given the cause variable.