SKILLSHARE ARTIFICIAL INTELLIGENCE ACADEMY 6 NAIVE BAYES CLASSIFICATION METHOD-iLLiTERATE

In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.

In the statistics and computer science literature, Naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.

In the first section you learn how to use python to classify output of your system with nonlinear structure .In this section you can classify:

- IRIS Flowers
- Pima Indians Diabetes Database
- Make your own Naive Bayes Algorithm