search for: minobsinnod

...ar.type=as.integer(var.type), var.monotone=as.integer(var.monotone), distribution=as.character(distribution.call.name), n.trees=as.integer(n.trees), interaction.depth=as.integer(interaction.depth), n.minobsinnode=as.integer(n.minobsinnode), n.classes = as.integer(nClass), shrinkage=as.double(shrinkage), bag.fraction=as.double(bag.fraction), nTrain=as.integer(nTrain), fit.old=as.double(NA),...

...iss sth here. Anyone has similar experience and can advise me how to implement cost-sensitive classification with gbm. model.gbm <- gbm.fit(tr[,1:DIM],tr.y,offset = NULL,misc = NULL,distribution = "bernoulli",w = tr.w,var.monotone = NULL,n.trees = NTREE,interaction.depth = TREEDEPTH,n.minobsinnode = 10,shrinkage = 0.05,bag.fraction = BAGRATIO,train.fraction = 1.0,keep.data = TRUE,verbose = TRUE,var.names = NULL,response.name = NULL); or model.gbm <- gbm(tr.y ~ .,distribution = "bernoulli",data=data.frame(cbind(tr[,1:DIM],tr.y)),weights = tr.w,var.monotone=NULL,n.trees=NTREE...

...et this to work. I note that there is no wrapper for gbm but that it is possible to use non-wrapped functions (like lm) without problem. Here's a snippet of code to illustrate. > data(mtcars) obj <- > gbm(mpg~disp+wt+carb,data=mtcars,distribution="gaussian",n.trees=1000,n.minobsinnode=5) > summary(obj) #just to demonstrate that gbm worked var rel.inf 1 disp 55.185469 2 wt 40.198605 3 carb 4.615926 # now let's find the best value for n.minobsinnode using tune > tune.obj <- > tune(gbm,mpg~disp+wt+carb,data=mtcars,distribution="gaussian",n.trees...

...shrinkage=0.001, n.trees=20000, bag.fraction=1, distribution="bernoulli") Here is the error: Error in gbm.fit(y = mytraindata[, 1], x = mytraindata[, -1], interaction.depth = 4, : The dataset size is too small or subsampling rate is too large: cRows*train.fraction*bag.fraction <= n.minobsinnode What might be the problem? Thanks a lot!

...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 = NULL) Does anybody know an easy way to parallelize the model (in this case it means simply having 4 cores on the same machine working on the problem)? Any sug...

...additive model, 2: two-way interactions, etc. + bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best + train.fraction = 0.5, # fraction of data for training, + # first train.fraction*N used for training + n.minobsinnode = 10, # minimum total weight needed in each node + cv.folds = 5, # do 5-fold cross-validation + keep.data=TRUE, # keep a copy of the dataset with the object + verbose=FALSE) # print out progress > > best.iter = gb...

...[3] "train.error" "valid.error" [5] "oobag.improve" "trees" [7] "c.splits" "bag.fraction" [9] "distribution" "interaction.depth" [11] "n.minobsinnode" "n.trees" [13] "nTrain" "response.name" [15] "shrinkage" "train.fraction" [17] "var.levels" "var.monotone" [19] "var.names" &q...

...<- sqrt(var(Y)/SNR) Y <- Y + rnorm(n,0,sigma) mydata <- data.frame(X,Y) #Fit Model (should take less than 20 seconds on an average modern computer) gbm1 <- gbm(formula = Y ~ X1 + X2 + X3 + X4 + X5, data=mydata, distribution = "gaussian", n.trees = 500, interaction.depth = 2, n.minobsinnode = 10, shrinkage = 0.1, bag.fraction = 0.5, train.fraction = 1, cv.folds=5, keep.data = TRUE, verbose = TRUE) ## Plot variable influence best.iter <- gbm.perf(gbm1, plot.it = T, method="cv") print(best.iter) summary(gbm1,n.trees=best.iter) # based on the estimated best number of trees...

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

...additive model, 2: two-way interactions, etc. + bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best + train.fraction = 0.5, # fraction of data for training, + # first train.fraction*N used for training + n.minobsinnode = 10, # minimum total weight needed in each node + cv.folds = 5, # do 5-fold cross-validation + keep.data=TRUE, # keep a copy of the dataset with the object + verbose=FALSE) # print out progress > > best.iter = gb...

...caret recognises? > getModelInfo("gbm")[["gbm"]]$parameters parameter class label 1 n.trees numeric # Boosting Iterations 2 interaction.depth numeric Max Tree Depth 3 shrinkage numeric Shrinkage 4 n.minobsinnode numeric Min. Terminal Node Size Is that a limitation of the caret package? Or is there something I'm not getting? -- ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~. ___ Patrick Connolly {~._.~} Great minds discuss ideas _( Y )_...