your question doesn't seem to specifically related to either R or random
forest. instead, it is about how to assign weights to training
observations.
On Sun, Mar 24, 2013 at 6:43 AM, Lorenzo Isella <lorenzo.ise...@gmail.com>wrote:
> Dear All,
> I am using randomForest to predict the final selling price of some items.
> As it often happens, I have a lot of (noisy) historical data, but the
> question is not so much about data cleaning.
> The dataset for which I need to carry out some predictions are fairly
> recent sales or even some sales that will took place in the near future.
> As a consequence, historical data should be somehow weighted: the older
> they are, the less they should matter for the prediction.
> Any idea about how this could be achieved?
> Please find below a snippet showing how I use the randomForest library (on
> a multi-core machine).
> Any suggestion is appreciated.
> Cheers
>
> Lorenzo
>
> ##############################**##############################**
> ###############
> rf_model <- foreach(iteration=1:cores,
> ntree = rep(50, 4),
> .combine = combine,
> .packages = "randomForest") %dopar%{
> sink("log.txt", append=TRUE)
> cat(paste("Starting iteration",iteration,"\n"))
> randomForest(trainRF,
> prices_train, ## mtry=20,
> nodesize=5,
> ## maxnodes=140,
> importance=FALSE, do.trace=10,ntree=ntree)
> ##############################**##############################**
> ###############
>
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--
==============================
WenSui Liu
Credit Risk Manager, 53 Bancorp
wensui....@53.com
513-295-4370
==============================
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