Description Usage Arguments Details Value Note References Examples
Perform the Boost algorithm on proc
with reweighter
and
aggregator
and monitor estimator performance with
analyzePerformance
.
1 2 3 4 5 6  boostBackend(B, reweighter, aggregator, proc, data, initialWeights, .procArgs,
analyzePerformance = defaultOOBPerformanceAnalysis,
.reweighterArgs = NULL, .aggregatorArgs = NULL,
.analyzePerformanceArgs = NULL, .subsetFormula = findFormulaIn(.procArgs),
.formatData = !is.null(.subsetFormula), .storeData = FALSE,
.calcBoostrPerformance = TRUE)

B 
the number of iterations to run. 
reweighter 
a boostr compatible reweighter function. 
aggregator 
a boostr compatible aggregator function. 
proc 
a boostr compatible estimation procedure. 
data 
the learning set to pass to 
initialWeights 
a vector of weights used for the first iteration of the ensemble building phase of Boost. 
.procArgs 
a named list of arguments to pass to 
.reweighterArgs 
a named list of arguments to pass to

.aggregatorArgs 
a named list of arguments to pass to

.storeData 
a boolean indicating whether the data should be stored in
the returned 
.calcBoostrPerformance 
a boolean indicating whether

.subsetFormula 
a 
.formatData 
a boolean indicating whether the data needs to be
reformatted via 
analyzePerformance 
a boostr compatible performance analyzer. 
.analyzePerformanceArgs 
a named list arguments to pass to

For the details behind this algorithm, check out the paper at http://pollackphoto.net/misc/masters_thesis.pdf
a "boostr
" object. The returned closure is the output of
aggregator
on the collection of estimators built during the iterative
phase of Boost. This is intended to be a new estimator, and hence accepts
the argument newdata
. However, the estimator also has attributes
ensembleEstimators 
An ordered list whose components are the trained estimators. 
reweighterOutput 
An ordered list whose components are the output of

performanceOnLearningSet 
The performance of the returned boostr object
on the learning set, as measure by 
estimatorPerformance 
An ordered list whose components are the output
of 
oobVec 
A rowmajor matrix whose ijth entry indicates if observation j was used to train estimator i. 
reweighter 
The reweighter function used. 
reweighterArgs 
Any additional arguments passed to 
aggregator 
The aggregator function used. 
aggregatorArgs 
Any additional arguments passed to 
estimationProcedure 
The estimation procedure used. 
estimationProcedureArgs 
Any additional arguments passed to

data 
The learning set. Only stored if 
analyzePerformance 
The performance analyzer used. 
analyzePerformanceArgs 
Any additional arguments passed to

subsetFormula 
The value of 
formatData 
The value of 
storeData 
The value of 
calcBoostrPerformance 
The value of 
initialWeights 
The initial weights used. 
The attributes can be accessed through the appropropriate
extraction function
.
wrapReweighter
, wrapAggregator
,
wrapPerformanceAnalyzer
, wrapProcedure
, and
buildEstimationProcedure
are all Wrapper Generators
designed to allow user implemented functions inside the boostBackend
.
These functions are intelligently called from inside boost
.
Thus, to minimize any sources of frustration, the recommended use of
boostBackend
is through boost
.
Steven Pollack. (2014). Boost: a practical generalization of AdaBoost (Master's Thesis). http://pollackphoto.net/misc/masters_thesis.pdf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Not run:
df < within(iris, {
Setosa < factor(2*as.numeric(Species == "setosa")  1)
Species < NULL
})
form < formula(Setosa ~ . )
df < model.frame(formula=form, data=df)
# demonstrate arcfs algorithm using boostr convenience functions
glmArgs < list(.trainArgs=list(formula=form, family="binomial"))
# format prediction to yield response in {1,1} instead of {0,1}
glm_predict < function(object, newdata) {
2*round(predict(object, newdata, type='response'))  1
}
Phi_glm < buildEstimationProcedure(train=glm, predict=glm_predict)
phi < boostBackend(B=3, data=df,
reweighter=adaboostReweighter,
aggregator=adaboostAggregator,
proc=Phi_glm,
.procArgs=glmArgs)
## End(Not run)

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