Are explaining and predicting the same? An age-old debate in philosophy of science started with Hempel & Oppenheim’s 1948 paper that equates the logical structure of predicting and explaining (saying that in effect they are the same, except that in explaining the phenomenon already happened while in prediction it hasn’t occurred). Later on it was recognized that the two are in fact very different. When it comes to statistical modeling, how are the two different? Do we model data differently when the goal is to explain than to predict? In a recent paper co-authored with Otto Koppius from Erasmus University, … Continue reading Good predictions by wrong model?
Somewhere in the early 90’s I started as a teaching assistant for the “intro to probability” course. Before introducing conditional probabilities, I recall presenting the students with the “Let’s make a deal” problem that was supposed to show them that their intuition is often wrong and therefore they should learn about laws of probability, and especially conditional probability and Bayes’ Rule. This little motivation game was highlighted in last week’s NYT with an extremely cool interactive interface: welcome to the Monty Hall Problem! The problem is nicely described in Wikipedia:Suppose you’re on a game show, and you’re given the choice … Continue reading Are conditional probabilities intuitive?
Although the call for this competition has been out for a while on KDnuggets.com, today is the day when the data and the task description are released. This data mining competition is aimed at students. The prizes probably might not sound that attractive to student (“participation in the KDD 2008, the world’s largest international conference for “Knowledge Discovery and Data Mining” (August 24-27, 2008 in Las Vegas)”, so I’d say the real prize is cracking the problem and winning! An interesting related story that I recently heard from Chris Volinsky from the Belkor team (who is currently in first place) … Continue reading Data Mining Cup 2008 releases data today