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Posted: September 6th, 2012 | Author: admin | Filed under: Uncategorized | 8 Comments » Recently I jumped in and taught myself how to do medium-sized data exploration and machine learning. (Excel-sized < My Data Set < Big Data)
(This post is elementary: it introduces a simple method of visualizing Bayesian calculations. In my defense, we've had other elementary posts before, and they've been found useful; plus, I'd really like this to be online somewhere, and it might as well be here.) I'll admit, those Monty-Hall - type problems invariably trip me up.
Source code for this article may be found here . Note for nerds: The code shown in this article may be incomplete and may not contain all the security checks you would usually perform in your code as it is given here for demonstration purposes only. Downloadable source code may contain bugs (there is no software without bugs at all).
Most marketing people have only a passing interaction with statistics, and often times only understand it as a measure of how it has impacted their daily life. One of the funny things people don’t realize is that there are two completely different competing schools of thought when it comes to statistics. Most people are familiar with frequentist statistics, having dealt with things like normal distribution, bell curves, and established probabilities.
A Bayes classifier is a simple statistical-based learning scheme. Advantages: Tiny memory footprint Fast training, fast learning Simplicity Often works surprisingly well
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Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. That's all. Just one equation.
How To Build a Naive Bayes Classifier
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