Description Usage Arguments Details Value Examples
View source: R/evaluate_predictions.r
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.
1 | evaluate.predictions(siamcat, verbose = 1)
|
siamcat |
object of class siamcat-class |
verbose |
integer, control output: |
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.
object of class siamcat-class with the
slot eval_data
filled
1 2 3 | data(siamcat_example)
siamcat_evaluated <- evaluate.predictions(siamcat_example)
|
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