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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