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Ruleset to Table

KNIME Java Snippet Nodes version 4.3.0.v202011191508 by KNIME AG, Zurich, Switzerland

Converts PMML RuleSet models (with firstHit rule selection method) to table containing the rules. This table is suitable for the Rule Engine (Dictionary) node as a rule input (even with PMML output generation). Use cases for this node: Convert the PMML model to ordinary table to

  • concatenate different rule tables
  • (row) filter out certain outcomes
  • re-level/recode outcomes


Split rules to condition and outcome columns
When checked, two columns will be created for the rules, Condition and Outcome, otherwise the rules will reside in a single column, Rule.
Add confidence and weight columns
From PMML the confidence and weight attributes are extracted to columns.
Add Record count and Number of correct statistics columns
In PMML, the recordCount and the nbCorrect attributes provide statistics about the input (training/test/validation) data, with this option, this information can be extracted to the columns: Record count and Number of correct
Use additional parentheses to document precedence rules
If checked the output will contain additional parenthesis around rule parts to clearly document precedence. For instance, NOT is a stronger operator than AND than OR - using parenthesis improves readability. Checking this option does not change any of the rule logic.
Provide score distibution record count in table with column name prefix
Information about the record count of score distribution is saved to the table with the specified prefix.
Provide score distibution probability in table with column name prefix
Information about the probabilities of score distribution is saved to the table with the specified prefix

Input Ports

The PMML RuleSet model

Output Ports

The table contains the rules' text (in single (Rule) or two columns (Condition, Outcome), the rule Confidence and Weight information and optionally the Record count (for how many rows did the ruleset matched when created) and Number of correct values where the outcome of the rule matched the expected label when the model was created.

Best Friends (Incoming)

Best Friends (Outgoing)


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