Bayes classifier and Naive Bayes tutorial (using the MNIST dataset) - Lazy Programmer

The Naive Bayes classifier is a simple classifier that is often used as a baseline for comparison with more complex classifiers. We will use the famous MNIST data set (pre-processed via PCA and normalized [TODO]) for this tutorial, so our class labels are {0, 1, …, 9}. If you're like me, you may have found this notation a little confusing at first. We can read the left side P(C X) as "the probability that the class is C given the data X". We can read the right side P(X C) as "the probability that the data X belongs to the class C". (this is called the "likelihood") And we can compute the probability that the class 0 given the data, probability that the class 1 given the data, etc. just by computing the probability of the data for each class (how well the data fits a model of each class).