Takes an input containing a binary activity (experimental/ground truth) and one or more prediction columns. For multi class classifications you should use the KNIME Scorer node. This node has been developed for binary classification and you must specify the value of active (positive) and inactive (negative). Values can be specified for equivocal and out of domain regardless of whether they are present in the prediction column.
Missing values are handled in the following ways: missing activity is ignored completely regardless of selection of "Missing out of domain". Selecting the missing out of domain option will increment the out of domain count when the prediction value is missing but the activity value is present.
Values that are not mapped to either active, inactive, equivocal or out of domain will be treated as errors and not contribute to the metric calculation.
Target values that do not match the active or inactive value specified are not included in the calculation.
Calculates:
Balanced accuracy: Sensitivity + Specificity / 2
Accuracy: TP + TN / 2
Sensitivity: TP / (TP + FN)
Specificity: TN / (TN + FP)
Precision aka Positive Predictivity (PPV): TP / (TP + FP)
Negative predictivity (NPV): TN / (TN + FN)
Recall: TP / (TP + FN)
F-Measure: 2 * ((precision * recall) / (precision + recall))
MCC: Matthews correlation coefficient / Karl Pearson's phi coefficient
Youden's J Statistic: sensitivity + specificity - 1
Balanced PPV: sensitivity / sensitivity + 1 - specificity
Balanced NPV: specificity / specificity + 1 - sensitivity
Coverage out of domain / total. The total included equivocal
Also outputs the counts for TP, FP, TN, FN, number of equivocals and number of out of domains and coverage (% not out of domain).
Note that the number of equivocals and number out of domain do not impact on the Cooper statistics (Sensitivity, specificity etc.)