Data Science: Implementing a Naive Bayesian Classifier using the Empirical Density.
This story is part of my Data Science series.
In my previous story (see here), I have provided an example implementation for a random forest based on a classification problem stemming from this set of data.
As one further approach for solving this problem I want to use a Naive Bayes classifier which implementation shall be the focus of this story.
In general, since the data set is quite large, the Naive Bayes approach is a good choice despite of its rather hard assumptions imposed on the data. You may find some more details on that in the theoretic outline here.
As a quick reminder, the Naive Bayes is based on the following relation of conditional probabilities:
P(C | X) = P(C) / P(X) * P(X | C)where C is the categorical outcome random variable and X the feature random variable.
As we see, in order to find an estimate for the outcome to be 1 based on the condition the feature to take value…
