Displaying 6 results from an estimated 6 matches for "boost_control".
2009 Sep 26
1
mboost_1.1-3 blackboost_fit (PR#13972)
...maxdepth = 2
),
fitmem = ctree_memory(
bd,
TRUE
),
family = GaussReg(),
control = boost_control(
mstop = 2
),
weights = NULL
)
Test case session on my computer:
> dt=expand.grid(y=c(2,3,4), x1=c(1,2), x2=c(1,2))
> library(mboost)
Loading required package: modeltools
Loading requ...
2010 Feb 03
0
mboost: how to implement cost-sensitive boosting family
...f * ifelse(y==1,10,1))
}
offset <- function (y, w)
{
p <- weighted.mean(y > 0, w)
1/(10+1) * log(10*p/1*(1 - p))
}
CSAdaExp <- Family(ngradient = ngradient, loss = loss, offset = offset);
model.blackboost <- blackboost(tr[,1:DIM], tr.y, family=CSAdaExp,
weights=tr.w, control=boost_control(mstop=100, nu=0.1),
tree_controls=ctree_control(teststat = "max",testtype =
"Teststatistic",mincriterion = 0,maxdepth = 10));
or
#loss <- function (y, f)
#{
# exp(-y * f * ifelse(y==1,COST_FN,COST_FP))
#}
#ngradient <- function (y, f, w = 1)
#{
# y * ifelse(y==1,CO...
2013 Jan 04
1
Predicting New Data -
I am having trouble predicting new data with a model created from package
mboost:
> mb1<-glmboost(as.formula(formula1),data=data_train,control=boost_control(mstop=400,nu=.1))
> f.predict<-predict(mb1,newdata=data_train)
Error in scale.default(X, center = cm, scale = FALSE) :
length of 'center' must equal the number of columns of 'x'
Ultimately I want to predict "data_test", but the orginal dataset won't even
wo...
2010 Feb 07
1
mboost: Interpreting coefficients from glmboost if center=TRUE
...ve
models . I am performing prediction on a binomial outcome, using a
linear function (glmboost). However, I am running into some confusion
regarding centering. (I am not aware of an mboost-specific mailing
list, so if the main R list is not the right place for this topic,
please let me know.)
The boost_control() function allows for the choice between center=TRUE
and center=FALSE. If I select center=FALSE, I am able to interpret the
coefficients just like those from standard logistic regression.
However, if I select center=TRUE, this is no longer the case. In
theory and in practice with my data, centering...
2012 Jul 23
1
mboost vs gbm
...issue is meaningful since the output of this regression needs to
be implemented in a production system, and mboost doesn't even expose the
ensembles.
# default params for blackboost are a gaussian loss, and maxdepth of 2
m.mboost = blackboost(Y ~ X1 + X2, data=tdata, weights=t.ipcw,
control=boost_control(mstop=100))
m.gbm = gbm(Y ~ X1 + X2, data=tdata, weights=t.ipcw,
distribution="gaussian", interaction.depth=2, bag.fraction=1, n.trees=2500)
# compare IPCW weighted squared loss
sum((predict(m.mboost, newdata=tdata)-tdata$Y)^2 * t.ipcw) <
sum((predict(m.gbm, newdata=tdata, n.trees=250...
2010 Mar 19
0
mboost: Interpreting coefficients from glmboost if center=TRUE
...ar function (glmboost). However, I am running into some confusion
> >> regarding centering. (I am not aware of an mboost-specific mailing
> >> list, so if the main R list is not the right place for this topic,
> >> please let me know.)
> >>
> >> The boost_control() function allows for the choice between center=TRUE
> >> and center=FALSE. If I select center=FALSE, I am able to interpret the
> >> coefficients just like those from standard logistic regression.
> >> However, if I select center=TRUE, this is no longer the case. In
&...