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Redfield & JU - Conformal Classification Experiment

Run experiment with different epsilon values Iterate all datasets Run 100 x 10 cross validation Run experiment with different epsilon values Iterate all datasets Run 10 cross validation for each epsilon Load the path to the 26 classification datasets Node 40Node 48String(Y)Node 71Y = Target columnNode 76Node 108Node 110Node 115Node 116Node 117Node 119Node 122Node 123Error Rate = Epsilon (variable)Node 152Node 180Node 181Node 195Define Epsilon values to useNode 197Add epsilon valueExtract dataset nameAdd dataset nameAdd epsilon valueAdd dataset nameLoad the path to the 27classification datasets IteratedatasetsRead currentdatasetMake targetcategoricalvariableGather all modelsresultsCreate standard target column nameY = Target columnPredicted valuesGather allcross-validationresultsEstimateconformalpredictionsCross-validationCalibration tableError Rate = EpsilonCalibration/TrainingVisualize resultsDefine Epsilon values to useAdd epsilon valueExtract dataset nameAdd dataset nameAdd epsilon valueAdd dataset nameIterate EpsilonGathercross-validationresultsper EpsilonNode 252Create standard target column nameY = Target columnRead currentdatasetExtract dataset nameIteratedatasetsLoad the path to the 27classification datasets Make targetcategoricalvariableNode 273Node 274Node 275Node 276Node 277Node 278Node 279Error Rate = EpsilonAdd dataset nameNode 282Node 283List Files/Folders Table Row ToVariable Loop Start File Reader Number To String Loop End Column Rename Path to String Random ForestPredictor X-Aggregator Counting Loop Start Random ForestLearner Loop End Conformal Scorer X-Partitioner Random ForestPredictor ConformalClassification ConformalPartitioning CSV Writer CSV Writer Summary tables Loop End Table Creator Table Row ToVariable Loop Start ConstantValue Column String Manipulation(Variable) ConstantValue Column ConstantValue Column ConstantValue Column List Files/Folders Table Row ToVariable Loop Start File Reader Number To String Loop End Column Rename Path to String Random ForestPredictor Random ForestLearner Loop End Conformal Scorer X-Partitioner Random ForestPredictor ConformalClassification ConformalPartitioning Summary tables Table Creator ConstantValue Column String Manipulation(Variable) ConstantValue Column ConstantValue Column ConstantValue Column Table Row ToVariable Loop Start Loop End Model to Cell Column Rename Path to String File Reader String Manipulation(Variable) Table Row ToVariable Loop Start List Files/Folders Number To String Column Filter Model Reader CSV Reader String Manipulation(Variable) Create File/FolderVariables Create File/FolderVariables Random ForestPredictor ConformalClassification ConstantValue Column Loop End Column Filter Pick best model andcalibration table Run experiment with different epsilon values Iterate all datasets Run 100 x 10 cross validation Run experiment with different epsilon values Iterate all datasets Run 10 cross validation for each epsilon Load the path to the 26 classification datasets Node 40Node 48String(Y)Node 71Y = Target columnNode 76Node 108Node 110Node 115Node 116Node 117Node 119Node 122Node 123Error Rate = Epsilon (variable)Node 152Node 180Node 181Node 195Define Epsilon values to useNode 197Add epsilon valueExtract dataset nameAdd dataset nameAdd epsilon valueAdd dataset nameLoad the path to the 27classification datasets IteratedatasetsRead currentdatasetMake targetcategoricalvariableGather all modelsresultsCreate standard target column nameY = Target columnPredicted valuesGather allcross-validationresultsEstimateconformalpredictionsCross-validationCalibration tableError Rate = EpsilonCalibration/TrainingVisualize resultsDefine Epsilon values to useAdd epsilon valueExtract dataset nameAdd dataset nameAdd epsilon valueAdd dataset nameIterate EpsilonGathercross-validationresultsper EpsilonNode 252Create standard target column nameY = Target columnRead currentdatasetExtract dataset nameIteratedatasetsLoad the path to the 27classification datasets Make targetcategoricalvariableNode 273Node 274Node 275Node 276Node 277Node 278Node 279Error Rate = EpsilonAdd dataset nameNode 282Node 283List Files/Folders Table Row ToVariable Loop Start File Reader Number To String Loop End Column Rename Path to String Random ForestPredictor X-Aggregator Counting Loop Start Random ForestLearner Loop End Conformal Scorer X-Partitioner Random ForestPredictor ConformalClassification ConformalPartitioning CSV Writer CSV Writer Summary tables Loop End Table Creator Table Row ToVariable Loop Start ConstantValue Column String Manipulation(Variable) ConstantValue Column ConstantValue Column ConstantValue Column List Files/Folders Table Row ToVariable Loop Start File Reader Number To String Loop End Column Rename Path to String Random ForestPredictor Random ForestLearner Loop End Conformal Scorer X-Partitioner Random ForestPredictor ConformalClassification ConformalPartitioning Summary tables Table Creator ConstantValue Column String Manipulation(Variable) ConstantValue Column ConstantValue Column ConstantValue Column Table Row ToVariable Loop Start Loop End Model to Cell Column Rename Path to String File Reader String Manipulation(Variable) Table Row ToVariable Loop Start List Files/Folders Number To String Column Filter Model Reader CSV Reader String Manipulation(Variable) Create File/FolderVariables Create File/FolderVariables Random ForestPredictor ConformalClassification ConstantValue Column Loop End Column Filter Pick best model andcalibration table

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Created by: loftuw
Created at: 2022-03-21
On NodePit since: 2022-05-04
Last update: 2024-04-25
Created with KNIME version: v4.5.2

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