Naive Bayes is a deceptively simple way to find answers to probability questions that involve many inputs. For example, if you're a website owner, you might be interested to know the probability that a visitor will make a purchase. That question has a lot of "what-ifs", including time on page, pages visited, and prior visits. Naive Bayes essentially allows you to take the raw inputs (i.e. historical data), sort the data into more meaningful chunks, and input them into a formula. …

Bayes’ Theorem is a way to calculate conditional probability. The formula is very simple to calculate, but it can be challenging to fit the right pieces into the puzzle. The first challenge comes from defining your event (A) and test (B); The second challenge is rephrasing your question so that you can work backwards: turning P(A|B) into P(B|A). The following image shows a…

A non-technical look at A/B testing, based on Dan Siroker & Pete Koomen's book, A / B Testing, The Most Powerful Way to Turn Clicks Into Customers. 

Perhaps the two most important points:

1. Make sure you are testing a clear hypothesis. For example., "Will adding a photo to the landing page…

Ensemble methods take several machine learning techniques and combine them into one predictive model. It is a two step process: 

1. Generate the Base Learners: Choose any combination of base learners, based on accuracy and diversity. Each base learner can produce more than one predictive model, if you change variables such as case weights, guidance parameters, or input space partitions.
2. Combine Estimates from the Base…