Displaying 3 results from an estimated 3 matches for "trainrf".
2013 Feb 03
3
RandomForest, Party and Memory Management
...ease see how I am using the Party and RandomForest packages. Any
comment is welcome and useful.
myparty <- cforest(SalePrice ~ ModelID+
ProductGroup+
ProductGroupDesc+MfgYear+saledate3+saleday+
salemonth,
data = trainRF,
control = cforest_unbiased(mtry = 3, ntree=300, trace=TRUE))
rf_model <- randomForest(SalePrice ~ ModelID+
ProductGroup+
ProductGroupDesc+MfgYear+saledate3+saleday+
salemonth,
data = trainRF,na.action = na.o...
2013 Mar 24
3
Parallelizing GBM
...amming.
Most of the time, I rely or randomForest for data mining large datasets.
I would like to give a try also to the gradient boosted methods in GBM,
but I have a need for parallelization.
I normally rely on gbm.fit for speed reasons, and I usually call it this
way
gbm_model <- gbm.fit(trainRF,prices_train,
offset = NULL,
misc = NULL,
distribution = "multinomial",
w = NULL,
var.monotone = NULL,
n.trees = 50,
interaction.depth = 5,
n.minobsinnode = 10,
shrinkage = 0.001,
bag.fraction = 0.5,
nTrain = (n_train/2),
keep.data = FALSE,
verbose = TRUE,
var.names = NULL,
response.name...
2013 Mar 24
1
Random Forest, Giving More Importance to Some Data
...s,
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)
###########################################################################