`LoggerInbagRisk.Rd`

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.

LoggerInbagRisk$new(logger_id, use_as_stopper, used_loss, eps_for_break)

`logger_id`

[`character(1)`

]Unique identifier of the logger.

`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 \(\mathcal{D} = \{(x^{(i)},\ y^{(i)})\ |\ i \in \{1, \dots, n\}\}\) and stores it into a vector. The empirical risk \(\mathcal{R}\) for iteration \(m\) is calculated by: $$ \mathcal{R}_\mathrm{emp}^{[m]} = \frac{1}{n}\sum\limits_{i = 1}^n L(y^{(i)}, \hat{f}^{[m]}(x^{(i)})) $$

**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.

# Used loss: log_bin = LossBinomial$new() # Define logger: log_inbag_risk = LoggerInbagRisk$new("inbag", FALSE, log_bin, 0.05) # Summarize logger: log_inbag_risk$summarizeLogger()#> Inbag risk logger: #> - Use logger as stopper: 0