# [R] gbm question

David Katz dkatz at tibco.com
Tue Nov 22 00:35:25 CET 2016

```R-Help,

Please help me understand why these models and predictions are different:

library(gbm)

set.seed(32321)
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)

set.seed(32321)
gbm.formula <-
gbm(Y~X1+X2+X3+X4+X5+X6,         # formula
data=data,                   # dataset
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 = 1,        # fraction of data for training,
# first train.fraction*N used for
training
n.minobsinnode = 10,         # minimum total weight needed in each
node
keep.data=TRUE,              # keep a copy of the dataset with the
object
verbose=FALSE)               # don't print out progress

set.seed(32321)
gbm.Fit <-
gbm.fit(x=data[,-1],y=Y,
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
nTrain=length(Y),
# first train.fraction*N used for
training
n.minobsinnode = 10,         # minimum total weight needed in each
node
keep.data=TRUE,              # keep a copy of the dataset with the
object
verbose=FALSE)               # don't print out progress

all.equal(predict(gbm.formula,n.trees=100), predict(gbm.Fit,n.trees=100))

>  "Mean relative difference: 0.3585409"

#all.equal(gbm.formula,gbm.Fit) no!

(Based on the package examples)

Thanks

*David Katz*| IAG, TIBCO Spotfire

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