Description Usage Arguments Details Value See Also Examples
Extracts the number of boosting iterations from a model that where performed or estimates the optimal number of boosting iterations.
1 2 3 4 5 |
object |
object of class |
opt |
logic. If |
... |
(not used a.t.m.) |
The calculated risk is the negative maximum log likelihood for each boosting step.
Applied to a model object of class cfboost
the function
mstop
can be used to extract the number of boosting iterations
that were performed (opt = FALSE
) or the optimal number of
boosting iterations opt = TRUE
with respect to the in-bag risk
or out-of-bag risk. In the first case, the risk is computed on the
learning sample, in the latter case it is computed on the validation
sample.
The samples can be specified by weights
that are given to
cfboost
. The risk that is to be computed is specified
in the call of cfboost
, to be more precise by the
boost_control
function. If an out-of-bag sample is
specified, the risk that is to be computed can be
set to both alternatives, if not, only the inbag risk is appropriate.
The function mstop.cv
is used to extract the
optimal boosting iteration from a cross-validated model (as
returned by cv
.
The (optimal) number of boosting iterations is returned.
boost_control
for the specification of
risk-type and cfboost
for the specification of
validation samples. See cv
for cross-validation.
1 | ## see for example ?cfboost and ?cv for usage of mstop()
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