Description Format Usage Arguments Details Fields Methods Examples
This class logs the inbag risk for a specific loss function. It is also possible to use custom losses to log performance measures. For details see the use case or extending compboost vignette.
S4
object.
1 | LoggerInbagRisk$new(use_as_stopper, used_loss, eps_for_break)
|
use_as_stopper
[logical(1)
]Boolean to indicate if the logger should also be used as stopper.
used_loss
[Loss
object]The loss used to calculate the empirical risk by taking the mean of the returned defined loss within the loss object.
eps_for_break
[numeric(1)
]This argument is used if the loss is also used as stopper. If the relative
improvement of the logged inbag risk falls above this boundary the stopper
returns TRUE
.
This logger computes the risk for the given training data \mathcal{D} = \{(x^{(i)},\ y^{(i)})\ |\ i \in \{1, …, n\}\} and stores it into a vector. The empirical risk \mathcal{R} for iteration m is calculated by:
\mathcal{R}_\mathrm{emp}^{[m]} = \frac{1}{n}∑\limits_{i = 1}^n L(y^{(i)}, \hat{f}^{[m]}(x^{(i)}))
Note:
If m=0 than \hat{f} is just the offset.
The implementation to calculate \mathcal{R}_\mathrm{emp}^{[m]} is done in two steps:
Calculate vector risk_temp
of losses for every observation for
given response y^{(i)} and prediction \hat{f}^{[m]}(x^{(i)}).
Average over risk_temp
.
This procedure ensures, that it is possible to e.g. use the AUC or any
arbitrary performance measure for risk logging. This gives just one
value for risk_temp
and therefore the average equals the loss
function. If this is just a value (like for the AUC) then the value is
returned.
This class is a wrapper around the pure C++
implementation. To see
the functionality of the C++
class visit
https://schalkdaniel.github.io/compboost/cpp_man/html/classlogger_1_1_inbag_risk_logger.html.
This class doesn't contain public fields.
summarizeLogger()
Summarize the logger object.
1 2 3 4 5 6 7 8 | # Used loss:
log.bin = LossBinomial$new()
# Define logger:
log.inbag.risk = LoggerInbagRisk$new(FALSE, log.bin, 0.05)
# Summarize logger:
log.inbag.risk$summarizeLogger()
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