Category: Machine Learning

Many of you might have heard of the concept “Wisdom of the Crowd”: when many people independently guess some quantity, e.g. the number of marbles in a jar glass, the average of their guesses is often pretty accurate – even though many of the guesses are totally off.

The same principle is at work in so-called ensemble methods, like bagging and boosting. If you want to know more about boosting and how to turn pseudocode of a scientific paper into valid R code read on…
Continue reading “Understanding AdaBoost – or how to turn Weakness into Strength”

A few weeks ago we published a post about using the power of the evolutionary method for optimization (see Evolution works!). In this post we will go a step further, so read on…
Continue reading “Symbolic Regression, Genetic Programming… or if Kepler had R”

One of the classic examples in data science (called data mining at the time) is the beer and diapers example: when a big supermarket chain started analyzing their sales data they encountered not only trivial patterns, like toothbrushes and toothpaste being bought together but also quite strange combinations like beer and diapers. Now, the trivial ones are reassuring that the method works but what about the more extravagant ones? Does it mean that young parents are alcoholics? Or that instead of breastfeeding they give their babies beer? Obviously, they had to get to the bottom of this.
Continue reading “Customers who bought…”