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Takes an input containing an activity (experimental/true) and multiple (or one) 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.
Target values that do not match the active or inactive value specified are not included in the calculation.
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))
Also outputs the counts for TP, FP, TN, FN, number of equivocals and number of out of domains and coverage (% not out of domain).