search for: gbm1

...0,1),5,5, byrow=T) n <- 2000 # obs X <- mvrnorm(n, rep(0, 5), cov.m) Y <- apply(X, 1, sum) SNR <- 10 # signal-to-noise ratio sigma <- 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 &l...

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]]

...following is the rel.inf value of 25 variables, but wen I plot, not all the variables are labeled. i.e. num_genes, wg, hydrophob_per etc are not labeled on the y-axis. also the variables are labeled vertically, can it be labeled horizontally just like the summary table? Thanks! > summary(gbm1, n.trees=best.iter, plotit=TRUE, order=TRUE, cBars=14) # based on the estimated best number of trees var rel.inf 1 NE 26.59040034 2 num_genes 20.81996803 3 NW 13.92791855 4 WG 11.69661160 5 WD 7.55712791 6 hydrophob_per 6.0274...

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE,