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.


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.
Missing Values
Choose how to treat missing values in either the reference or prediction column. Default is to ignore them (treat them as if the row did not exist). Alternatively, you can expect the table to not contain missing values in these two columns. If they do, the node will fail during execution.

Input Ports

Table containing at least two columns to compare.

Output Ports

The confusion matrix.
The accuracy statistics table.


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.




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