Scorer
Compares two columns by their attribute value pairs and shows the
confusion matrix, i.e. how many rows of which attribute and their
classification match.
Additionally, it is possible to hilight cells of this matrix to
determine the underlying rows.
The dialog allows you to select two columns for comparison;
the values from the first selected column are represented in the
confusion matrix's rows and the values from the second column by the
confusion matrix's columns. The output of the node is the confusion
matrix with the number of matches in each cell.
Additionally, the second out-port reports a number of
accuracy statistics such as True-Positives, False-Positives,
True-Negatives, False-Negatives, Recall, Precision, Sensitivity,
Specificity, F-measure, as well as the overall accuracy and Cohen's kappa.
Dialog Options
- First column
- The first column represents the real classes of the data.
- Second column
- The second column represents the predicted classes of the data.
- Sorting strategy
- Whether to sort the labels according to their appearance, or use the lexical/numeric ordering.
- Reverse order
- Reverse the order of the elements.
- Use name prefix
- The scores (i.e. accuracy, error rate, number of correct and wrong classification) are exported
as flow variables with a hard coded name. This option allows you to define a prefix for these variable identifiers so that name
conflicts are resolved.
Ports
Input Ports
0 |
Table containing at least two columns to compare. |
Output Ports
0 |
The confusion matrix. |
1 |
The accuracy statistics table. |
Views
- Confusion Matrix
-
Displays the confusion matrix in a table view. It is possible
to hilight cells of the matrix which propagates highlighting
to the corresponding rows. Therefore, it is possible for example to
identify wrong predictions.
This node is contained in KNIME Base Nodes
provided by KNIME GmbH, Konstanz, Germany.