Spark 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. 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.

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 lexically or numerically.

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.

Views

Confusion Matrix

Displays the confusion matrix in a table view.

Workflows

• 01_Spark_MLlib_Decision_TreeKNIME Hub
• 01_Spark_MLlib_Decision_TreeKNIME Hub
• 01_Spark_MLlib_Decision_TreeKNIME Hub
• 017101_Spark_MLlib_Decision_TreeKNIME Hub
• 03_DatabricksExampleKNIME Hub
• Show all 33 workflows

Links

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Developers