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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: k-fold cross validation |

Date |
Fri, 15 Feb 2008 11:32:15 -0500 |

1. see the jackknife command for the extreme version of this 2. you may prefer to use bootstrap -- see that command Rich Nalin Payakachat wrote:

Hi, I would like to perform k-fold cross validation using Stata. Here are explanation for k-fold (http://www.cs.cmu.edu/~schneide/tut5/node42.html): K-fold cross validation is one way to improve over the holdout method. The data set is divided into k subsets, and the holdout method is repeated k times. Each time, one of the k subsets is used as the test set and the other k-1 subsets are put together to form a training set. Then the average error across all k trials is computed. The advantage of this method is that it matters less how the data gets divided. Every data point gets to be in a test set exactly once, and gets to be in a training set k-1 times. The variance of the resulting estimate is reduced as k is increased. The disadvantage of this method is that the training algorithm has to be rerun from scratch k times, which means it takes k times as much computation to make an evaluation. A variant of this method is to randomly divide the data into a test and training set k different times. The advantage of doing this is that you can independently choose how large each test set is and how many trials you average over. If anybody could help, I would deeply appreciate it. Thank you so much. Nalin

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**Follow-Ups**:**RE: st: k-fold cross validation***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**References**:**st: k-fold cross validation***From:*Nalin Payakachat <npayakac@purdue.edu>

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