Sent to my inbox. The first example in the help file for gbm fails on some runs, sometimes runs ok.

The issue is caused by one of the models in cv.models having all NaNs or Infs in the valid.error element. Those values come out of the .Call("gbm" call looking like that in gbm.fit.R

N <- 1000 X1 <- runif(N) X2 <- 2_runif(N) X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1]) X4 <- factor(sample(letters[1:6],N,replace=TRUE)) X5 <- factor(sample(letters[1:3],N,replace=TRUE)) X6 <- 3_runif(N) mu <- c(-1,0,1,2)[as.numeric(X3)]

SNR <- 10 # signal-to-noise ratio Y <- X11.5 + 2 * (X2.5) + mu sigma <- sqrt(var(Y)/SNR) Y <- Y + rnorm(N,0,sigma)

introduce some missing values

X1[sample(1:N,size=500)] <- NA X4[sample(1:N,size=300)] <- NA

data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)

gbm1 <- gbm(Y~X1+X2+X3+X4+X5+X6, # formula data=data, # dataset var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,

+1: monotone increase,

  #  0: no monotone restrictions
  distribution="gaussian",     # see the help for other choices
  n.trees=1000,                # number of trees
  shrinkage=0.05,              # shrinkage or learning rate,
  #0.001 to 0.1 usually work
  interaction.depth=3,         # 1: 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 = 3,                # do 3-fold cross-validation
  keep.data=TRUE,              # keep a copy of the dataset with the object
  verbose=FALSE,               # don't print out progress
  n.cores=1)                   # use only a single core (detecting #cores is

error-prone, so avoided here)

harrysouthworth / gbm

example code fails #14

introduce some missing values

+1: monotone increase,

error-prone, so avoided here)