When I hear the word "ensemble", I think of two things: the grand canonical ensemble, and the brass ensemble. But today we're taking a look at ensemble learning, which is the canonical way that the grand top brass of data scientists augment and combine models. The need for ensembling arises when you're working at the cutting edge and a good model is not available. One model might anticipate a fraction of the cases, another a different fraction, and a third a different set again...

Let There be a Lighthouse In the preceding post we attempted to locate a queer lighthouse from the incident locations of its uniformly angular distributed flashes upon a straight beach. The geometry of the scenario is shown below. It turned out to be a difficult problem, because the flashpoints assume a Cauchy distribution, which makes the intuitively appealing sample mean totally useless. In this post, we track down the lighthouse by applying sound principles of probabili...

A Light Under a Bushel You're walking along a straight beach at night and you happen to know there's a lighthouse somewhere out to sea, shrouded in darkness on a rocky island. But it is no ordinary lighthouse: it emits flashes in random directions, favouring no angle over any other. Fortuitously, you have a set of sensors rigged up along the coastline that register the incident locations of any flashes that hit the beach. Where is the lighthouse? It turns out that this little pr...