Description Usage Arguments Details Value See Also
View source: R/adaboostReweighter.R
Implements a slightly modified version of the reweighter described in the Adaboost.M1 algorithm.
1  adaboostReweighter(prediction, response, weights, ...)

prediction 
a vector of predictions. 
response 
a vector whose ith component is the true
response for the ith component of 
weights 
a vector of weights. They don't necessarily need to sum to 1. 
... 
implemented to allow reweighter to accept its output as its input. 
The modification of the reweighter comes in to play when
ε = 0. This is when the esimator correctly
classifies every observation in the learning set. Consequently, we're
supposed to define
α = log(1ε)  log(ε)
However, this is +∞, which is not a number R is used
to working with. To work around this, we have to create a conditional statement
that sets alpha < log(.Machine$double.xmax)
and let the algorithm
proceed as originally described. The effect of this modification is the following:
the update that's supposed to be made to weights
, which is a
function of alpha
, effectively keeps weights
as they were
before.
if you pair this reweighter with adaboostAggregator
then
the estimator associated to this very large alpha
now has tremendous
weight inside the weighted sum in the aggregator. This isn't, necessarily,
a bad thing – the estimator classified every observation in data
correctly.
For internal bookkeeping, this function is inherits from the
'reweighter
' class. It returns a named list with components
weights 
the updated weights calculated from the input weights,

alpha 
performance measure of 
Other adaboost: adaboostAggregator
Other reweighters: arcfsReweighter
;
arcx4Reweighter
; boost
,
boost.function
, boost.list
;
vanillaBagger
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