Scala and Spark for Big Data Analytics

My name is Bayes, Naive Bayes

I'm Bayes, Naive Bayes (NB). I'm a successful classifier based upon the principle of maximum a posteriori (MAP). As a classifier, I am highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. I have several properties, for example, I am computationally faster, if you can hire me to classify something I'm simple to implement, and I can work well with high-dimensional datasets. Moreover, I can handle missing values in your dataset. Nevertheless, I'm adaptable since the model can be modified with new training data without rebuilding the model.

In Bayesian statistics, a MAP estimate is an estimate of an unknown quantity that equals the mode of ...