# 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.

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
# 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?