Imagine a newborn witnessing his first sunset. Being new to this world, he doesn't know whether the sun will rise again. Making a guess, he gives the chance of a sunrise even odds and places a black marble in a bag that represents no sunrise and a white marble that represents a sunrise. As each day passes, the child places in the bag a marble based on the evidence he witnesses—in this case, a white marble for each sunrise. Over time, the black marble becomes lost in a sea of white, and the child can say with near certainty that the sun will rise each day.

This was the example posed by Reverend Thomas Bayes in his 1763 paper establishing the methodology that is now one of the fundamental principles of modern Machine Learning. This is the foundation of the Microsoft Naïve Bayes algorithm. This is one of the least resource-intensive algorithms and is often used for the initial analysis of data so that we get an idea about the trends presented in the data...