Converts (a single) decision tree model to PMML RuleSet model and also to a table containing the rules in a textual form. The resulting rules are independent of each other, the order of rules is not specified, can be changed. Missing value strategies are ignored, it will always evaluate to missing value.
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(It will create columns with missing values.)","optional":false},{"name":"Add Record count and Number of correct statistics columns","description":"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","optional":false},{"name":"Use additional parentheses to document precedence rules","description":"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.","optional":false},{"name":"Provide score distribution record count in PMML","description":"PMML will store the information obtained from the rules on record counts of score distribution (how many times the different values were present in the training data, not necessarily integer)","optional":false},{"name":"Provide score distribution record count in table with column name prefix","description":"Information about the record count of score distribution is saved to the table with the specified prefix.","optional":false},{"name":"Record count column prefix","description":"Prefix for the score distribution record count columns.","optional":false},{"name":"Provide score distribution probability in PMML","description":"PMML will store the information obtained from the rules on probabilities of score distribution (in the training data what the probability was of certain values for that decision tree leaf)","optional":false},{"name":"Provide score distribution probability in table with column name prefix","description":"Information about the probabilities of score distribution is saved to the table with the specified prefix.","optional":false},{"name":"Probability column prefix","description":"Prefix for the score distribution probability columns.","optional":false}]}],"links":[],"kudos":[],"kudosCount":0,"bundleInformation":{"bundleVersion":"5.12.0.v202606171353","bundleName":"KNIME Java Snippet Nodes","bundleSymbolicName":"org.knime.jsnippets","bundleVendor":"KNIME AG, Zurich, Switzerland","featureSymbolicName":"org.knime.features.javasnippet","featureName":"KNIME Javasnippet","featureVersion":"5.12.0.v202606171353","featureVendor":"KNIME AG, Zurich, Switzerland","extensionId":"*hXfYFyb7ZzM0xseG"},"frequentSuccessors":[{"id":"*mTvdLptC3aqDqGrI","factoryName":"org.knime.base.node.mine.decisiontree2.predictor2.DecTreePredictorNodeFactory","probability":0.13812154696132597},{"id":"*OF3oP5KCNWP8Fkn2","factoryName":"org.knime.base.node.rules.engine.pmml.PMMLRuleSetPredictorNodeFactory","probability":0.09944751381215469},{"id":"*2fI5rIsFDCFPHc0R","factoryName":"org.knime.ext.jep.JEPNodeFactory","probability":0.055248618784530384},{"id":"*orOzsHYPnOJsY3H9","factoryName":"org.knime.base.node.mine.decisiontree2.image.DecTreeToImageNodeFactory","probability":0.049723756906077346},{"id":"*bDUBuscC47gFw7rG","factoryName":"org.knime.base.node.rules.engine.totable.RulesToTableNodeFactory","probability":0.03867403314917127},{"id":"*j-Kmco8ue7gKNE7e","factoryName":"org.knime.ext.md.RuleViewerNodeFactory","probability":0.03314917127071823},{"id":"*JiJhJpgkWWuOfG2s","factoryName":"org.knime.ext.poi2.node.write2.XLSWriter2NodeFactory","probability":0.03314917127071823},{"id":"*IPAOkJNeJy0B8rPZ","factoryName":"org.knime.base.node.preproc.cellsplit.CellSplitterNodeFactory","probability":0.027624309392265192},{"id":"*0Y5y2-u7yUx5ohSu","factoryName":"org.knime.base.node.rules.engine.pmml.PMMLRuleEditorNodeFactory","probability":0.022099447513812154},{"id":"*14A00VB0oh4by3NE","factoryName":"org.knime.base.node.io.pmml.write.PMMLWriterNodeFactory","probability":0.022099447513812154}]}