著者
小林 淳一 高本 和明 Kobayashi Junichi Komoto Kazuaki
雑誌
データマイニングと統計数理研究会(第 12 回)

Stochastic gradient boosting is a kind of the boosting methods invented by Jerome H.Friedman and it is known to be a very powerful method for making predictive models in some cases. In fact, FEG wins the second prize in KDD Cup 2009 by using this method. We survey the methodology of stochastic gradient boosting and introduce our analytical procedure in KDD Cup 2009. It is a good example where stochastic gradient boosting shows its effectiveness.