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.
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))
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.)
Options
Output Ports
- Performance metrics on the selected prediction columns. RowID is the column header of the selected columns.
- 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.
Popular Predecessors
Number To String9 %- Java Snippet8 %
Joiner6 %Row Splitter (deprecated)5 %- X-Aggregator5 %
- Decision Tree Predictor4 %
Scorer3 %Column Rename3 %Random Forest Predictor2 %- Reference Row Filter2 %
- GroupBy2 %
- Weka Predictor (3.7)2 %
Gradient Boosted Trees Predictor2 %Random Forest Learner2 %- Concatenate2 %
Naive Bayes Predictor1 %- MultiLayerPerceptron Predictor1 %
- Column Resorter1 %
- Rule-based Row Splitter1 %
Table Row to Variable1 %File Reader1 %- Table Creator1 %
- Random Forest Predictor1 %
Concatenate (Optional in)1 %- Column Aggregator1 %
- Column Filter1 %
- Rule-based Row Filter1 %
CSV Reader1 %Regression Predictor< 1 %Numeric Scorer< 1 %PMML Ensemble Predictor< 1 %- Strings to Binary Objects< 1 %
- Fuzzy Rule Predictor< 1 %
- SVM Predictor< 1 %
- String Manipulation< 1 %
List Files< 1 %- SDF Reader< 1 %
- Group Loop Start< 1 %
Logistic Regression Learner< 1 %- Numeric Binner< 1 %
String To Number< 1 %Column Appender< 1 %- Constant Value Column< 1 %
- Normalizer< 1 %
- Missing Value< 1 %
PMML Predictor< 1 %- Result< 1 %
Naive Bayes Learner< 1 %- Tree Ensemble Predictor< 1 %
- Number to String< 1 %
- Binned Performance< 1 %
- Substructure Search< 1 %
- Loop End (Column Append)< 1 %
- PNN Predictor< 1 %
- Entropy Scorer< 1 %
Random Forest Predictor< 1 %- Tree Ensemble Predictor (Regression)< 1 %
- Nominal Value Row Filter< 1 %
- Nominal Value Row Splitter< 1 %
Row Filter (deprecated)< 1 %- Reference Row Splitter< 1 %
- Normalizer (Apply)< 1 %
- Rule Engine< 1 %
- Math Formula< 1 %
Excel Reader (XLS)< 1 %- Java Snippet Row Filter< 1 %
- Category to Class< 1 %
Dictionary tagger< 1 %- String Input (legacy)< 1 %
- Normalize Plates (POC)< 1 %
- Naive Bayes Predictor< 1 %
K Nearest Neighbor< 1 %- K Nearest Neighbor< 1 %
- Logistic Regression Learner< 1 %
- Regression Predictor< 1 %
Random Forest Learner< 1 %- Tree Ensemble Learner< 1 %
Tree Ensemble Predictor< 1 %- Random Forest Predictor (Regression)< 1 %
- Column Comparator< 1 %
- Cross Joiner< 1 %
- Equal Size Sampling< 1 %
- Numeric Row Splitter< 1 %
- String Replacer< 1 %
- Parallel Chunk End< 1 %
K Nearest Neighbor (Distance Function)< 1 %- H2O to Table< 1 %
- LIBSVMPredictor< 1 %
Excel Reader (XLS)< 1 %- Java Snippet Row Splitter< 1 %
- Binary Image Operations< 1 %
- Discretise< 1 %
Binary Scorer< 1 %- Fingerprints< 1 %
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