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 make.predictions.

evaluate.predictions(siamcat, verbose = 1)

Arguments

siamcat

object of class siamcat-class

verbose

integer, control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1

Value

object of class siamcat-class with the slot eval_data filled

Details

This functions calculates several metrices for the predictions in the pred_matrix-slot of the siamcat-class-object. The Area Under the Receiver Operating Characteristic (ROC) Curve (AU-ROC) and the Precision-Recall Curve will be evaluated and the results will be saved in the eval_data-slot of the supplied siamcat-class-object. The eval_data-slot contains a list with several entries:

  • $roc - average ROC-curve across repeats or a single ROC-curve on complete dataset (see roc);

  • $auroc - AUC value for the average ROC-curve;

  • $prc - list containing the positive predictive value (precision) and true positive rate (recall) values used to plot the mean PR curve;

  • $auprc - AUC value for the mean PR curve;

  • $ev - list containing for different decision thresholds the number of false positives, false negatives, true negatives, and true positives.

For the case of repeated cross-validation, the function will additonally return

  • $roc.all - list of roc objects (see roc) for every repeat;

  • $auroc.all - vector of AUC values for the ROC curves for every repeat;

  • $prc.all - list of PR curves for every repeat;

  • $auprc.all - vector of AUC values for the PR curves for every repeat;

  • $ev.all - list of ev lists (see above) for every repeat.

Examples

data(siamcat_example) siamcat_evaluated <- evaluate.predictions(siamcat_example)
#> Evaluated predictions successfully.