This class logs the out of bag 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.


LoggerOobRisk$new(logger_id, use_as_stopper, used_loss, eps_for_break,
  patience, oob_data, oob_response)


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.

oob_data [list]

A list which contains data source objects which corresponds to the source data of each registered factory. The source data objects should contain the out of bag data. This data is then used to calculate the prediction in each step.

oob_response [numeric]

Vector which contains the response for the out of bag data given within the list.


This logger computes the risk for a given new dataset \(\mathcal{D}_\mathrm{oob} = \{(x^{(i)},\ y^{(i)})\ |\ i \in I_\mathrm{oob}\}\) and stores it into a vector. The OOB risk \(\mathcal{R}_\mathrm{oob}\) for iteration \(m\) is calculated by: $$ \mathcal{R}_\mathrm{oob}^{[m]} = \frac{1}{|\mathcal{D}_\mathrm{oob}|}\sum\limits_{(x,y) \in \mathcal{D}_\mathrm{oob}} L(y, \hat{f}^{[m]}(x)) $$


  • If \(m=0\) than \(\hat{f}\) is just the offset.

  • The implementation to calculate \(\mathcal{R}_\mathrm{emp}^{[m]}\) is done in two steps:

    1. Calculate vector risk_temp of losses for every observation for given response \(y^{(i)}\) and prediction \(\hat{f}^{[m]}(x^{(i)})\).

    2. 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 doesn't contain public fields.



Summarize the logger object.


# Define data: X1 = cbind(1:10) X2 = cbind(10:1) data_source1 = InMemoryData$new(X1, "x1") data_source2 = InMemoryData$new(X2, "x2") oob_list = list(data_source1, data_source2) set.seed(123) y_oob = rnorm(10) # Used loss: log_bin = LossBinomial$new() # Define response object of oob data: oob_response = ResponseRegr$new("oob_response", as.matrix(y_oob)) # Define logger: log_oob_risk = LoggerOobRisk$new("oob", FALSE, log_bin, 0.05, 5, oob_list, oob_response) # Summarize logger: log_oob_risk$summarizeLogger()
#> Out of bag risk logger: #> - Use logger as stopper: 0