R help - Mar 2013 - Random Forest, Giving More Importance to Some Data

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)
###########################################################################

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.isella@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.liu@53.com
513-295-4370
=============================
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