This function takes the correct labels and predictions for all samples and evaluates the results using the
Area Under the Receiver Operating Characteristic (ROC) Curve (AU-ROC)
and the Precision-recall Curve (PR)
as metric. Predictions can be supplied either for a single case or as matrix after resampling of the dataset.
Prediction results are usually produced with the function plm.predictor.
eval.result(label, pred)
label | label object |
---|---|
pred | prediction for each sample by the model, should be a matrix with dimensions |
list containing
$roc.average
average ROC-curve across repeats or a single ROC-curve on complete dataset;
$auc.average
AUC value for the average ROC-curve;
$ev.list
list of length(num.folds)
, containing for different decision thresholds the number of false positives, false negatives, true negatives, and true positives;
$pr.list
list of length(num.folds)
, containing the positive predictive value (precision) and true positive rate (recall) values used to plot the PR curves;
. If prediction
had more than one column, i.e. if the models has been trained with several repeats, the function will additonally return
$roc.all
list of roc objects (see roc) for every repeat;
$aucspr
vector of AUC values for the PR curves for every repeat;
$auc.all
vector of AUC values for the ROC curves for every repeat