You're probably familiar with the classic Travelling Salesman problem: given (say) 20 cities, what is shortest route you can take that passes through all 20 cities and returns to the starting point? It's a difficult problem to solve, because you need to try all possible routes to find the minimum, and there are a LOT of possibilities. For a 20-city tour there are more than 1 trillion trillion routes to try — and that's a fairly small problem!
A quick heads up that if you'd like to get a great introduction to doing data science with the R language, Joe Rickert will be giving a free webinar next Thursday, September 25: Data Science with R. Regular readers of the blog will be familiar with Joe's posts on this topic.
While preparing for the DataWeek R Bootcamp that I conducted this week I came across the following gem. This code, based directly on a Max Kuhn presentation of a couple years back, compares the efficacy of two machine learning models on a training data set.
Google has just released a new package for R: CausalImpact. Amongst many other things, this package allows Google to resolve the classical conundrum: how can we asses the impact of an intervention (for example, the effect of an advertising campaign on website clicks) when we can't know what would have happened if we hadn't run the campaign?