Meaning of output terms in gbm package? I am using gbm package for classification. As expected, the results is good. But I am trying to understand the output of the classifier.
There are five terms in output. 
`Iter   TrainDeviance   ValidDeviance   StepSize   Improve`

Could anyone explain the meaning of each term, especially the meaning of Improve.
 A: You should find these are related to determining the best value for the number of basis functions  - i.e. iterations - i.e. number of trees in the additive model. I cant find documentation describing exactly what these are but here is my best guess and maybe someone else can comment.
Take the following from the manual:
library(gbm)
# A least squares regression example
# create some data
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 <- X1**1.5 + 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)
# fit initial model
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", # bernoulli, adaboost, gaussian,
# poisson, coxph, and quantile available
n.trees=3000, # number of trees
shrinkage=0.005, # 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 = 5, # do 5-fold cross-validation
keep.data=TRUE, # keep a copy of the dataset with the object
verbose=TRUE) # print out progress

The number of iterations (Iter) is 3000, which is the number of trees selected to be built (1 to 3000 although not every one is shown). The full process is repeated 5 times by the way because we selected cv.folds=5. 
StepSize is the shrinkage or learning rate selected (0.005 here).
I believe that Improve is the reduction in the deviance (loss function) by adding another tree and is calculated using the out-of-bag (OOB) records (note it will not be calculated if bag.fraction is not <1).
Then for each iteration, the TrainDeviance   ValidDeviance is the value of the loss function on the training data and hold out data (a single hold out set). The ValidDeviance will not be calculated if train.fraction is not <1.
Have you seen this which describes the 3 types of methods for determining the optimal number of trees?
