Naïve Bayes is a supervised learning to do classification based on categorical parameters using Bayes Theorem. If you want to read about basic Machine Learning, please refer to here, and come back to this article again later. Still remember how to calculate probability from high school lesson? For instance the probability of a die to show 5 is 1/6. It can finally be useful in machine learning.
It is called “naïve” because assumes mutual independence among the predictors. For example, monkey is identified to have 2 arms, be brown color, and be good at jumping. All there characteristic contribute independently to identify that an animal is a monkey despite they actually depend on one another.
Continue reading “Naïve Bayes”
Data Science and GIS 