Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predcitors) in a learning problem. Maxumum-likelihood training can be done by evaluting a closed-form exporession, which takes linear time, rather tahn by expensive iterative approximation as used for many other typs of classifier. Wikipedia

If you ask yourself what’s the most important thing in machine learning, what’s your answer? All data scientist would have different answers.

supervised unsupervised feature label … 머신러닝을 시작하게 되면 새로 배워야 하는 용어들이 많죠? 하지만 이러한 용어들을 자신의 개념으로 잘 정리하는 것이 참 중요합니다. 왜냐하면, 우리가 앞으로 배우게 될 머신러닝의 기초가 되기 때문이죠…