This function compares the predictions (from
[make.predictions]) and true labels for all samples and evaluates
the results.

`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

## Binary classification problems

This function calculates several metrics 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 additionally
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.

## Regression problems

This function calculates several metrics for the evaluation of predictions
and will store the results in the `eval_data`

-slot of the supplied
siamcat-class objects. The `eval_data`

-slot will contain:

`r2`

- the mean R squared value across repeats or a single
R-squared value on the complete dataset;

`mae`

- them mean absolute error of the predictions;

`mse`

- the mean squared error of the predictions.

For the case of repeated cross-validation, the function will additionally
compute all three of these measures for the individual cross-validation
repeats and will store the results in the `eval_data`

slot as
`r2.all`

, `mae.all`

, and `mse.all`

.

## Examples

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