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).
Note that the number of equivocals and number out of domain do not impact on the Cooper statistics (Sensitivity, specificity etc.)
- Type: Data Target column (needs to be binary) and at least 1 prediction column
- Type: Data Performance metrics on the selected prediction columns. RowID is the column header of the selected columns.
- Type: Data Rows containing at least one missing value in a prediction or target column. Where the target column activity is present then it will check add the prediction for each column that is selected and that contains a prediction.
Community Nodes > Lhasa Limited > Generic > Scoring
Make sure to have this extension installed:
Lhasa public plugin
Update site for KNIME Analytics Platform 3.7:
KNIME Community Contributions (3.7)