## 550Re: AI-GEOSTATS: cross-validation

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• Mar 26, 2002
Dear Ercan

Cross validation could for example be performed by discarding a single
observation from the dataset and predicting this single observation based on
the rest of the dataset and with the proposed model. This is repeated for all
the observations in the dataset, and as a measure of the goodness of the model
you could calculate

GOM = sum[(observation - predicted)^2]/n

If you have n observations this procedure is referred to as n-fold cross
validation. Altenatively you could split your dataset into 10 parts,
then discarding one part and predicting this part based on the other 9
parts and the proposed model, would give you 10-fold cross validation.

If you want to compare different models, off course the one which gives
the lowest GOM - value is the best for predicting the variable.

Best regards / Venlig hilsen

Søren Lophaven
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On Mon, 25 Mar 2002, Ercan Yesilirmak wrote:

> Dear list members
>
> My question is as folows:
>
> For a variable after getting a number of models all of
> which seem well, how to decide which one is the best
> among them based on cross-validation results.
> i.e., How to use cross-validation results of a model
> to compare with those of others?
>
> Best regards
> Ercan Yesilirmak
>
>
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