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Model Training 2.0

Performance extraction and evaluation of accuracy and error for the selection of the model to train for deployment. Data acquisition and partitioning Preliminary execution of featureselection through a univariate filter tested on the SVM(Puk) Model Section dedicated to parameteroptimizationOptimization of the complexity parameter C withthe maximization of the F1-Measure through aparameter optimization loop. Execution of the selected modelsboth in holdout and cross validation with K=10, toperform a deeper comparison Section dedicated to training the model with the optimal parameter, and capturing the workflow for future deployment. Node 1Node 2Node 3Node 12Node 16Node 92Node 95Node 101Node 180Node 183Node 184Node 197Node 201Node 202Node 203Node 204Node 205Node 209Node 217Node 218Node 219Node 220Node 221Node 222Node 223Node 224Node 225Node 226Node 227Node 229Node 230Node 231Node 232Node 233 Excel Reader Number to String Partitioning Weka Predictor(3.7) SMO (3.7) Confidence Intervalof the Error Extraction ofthe Accuracy Error Extraction Data Preprocessing CaptureWorkflow Start CaptureWorkflow End Data Preprocessing(Apply) ParameterOptimization PreliminaryFeature Selection SVM in C-val(All Attributes) SVM in C-Val(Selected Features) ROC Curve Naive Bayes inC-Val and Holdout Box Plot Box Plot Box Plot ROC Curve Table Space Connector Workflow Writer Table Writer Table Writer Table Writer Table Writer Space Connector Rule Engine Rule Engine Table Writer Column Renamer Column Filter Performance extraction and evaluation of accuracy and error for the selection of the model to train for deployment. Data acquisition and partitioning Preliminary execution of featureselection through a univariate filter tested on the SVM(Puk) Model Section dedicated to parameteroptimizationOptimization of the complexity parameter C withthe maximization of the F1-Measure through aparameter optimization loop. Execution of the selected modelsboth in holdout and cross validation with K=10, toperform a deeper comparison Section dedicated to training the model with the optimal parameter, and capturing the workflow for future deployment. Node 1Node 2Node 3Node 12Node 16Node 92Node 95Node 101Node 180Node 183Node 184Node 197Node 201Node 202Node 203Node 204Node 205Node 209Node 217Node 218Node 219Node 220Node 221Node 222Node 223Node 224Node 225Node 226Node 227Node 229Node 230Node 231Node 232Node 233 Excel Reader Number to String Partitioning Weka Predictor(3.7) SMO (3.7) Confidence Intervalof the Error Extraction ofthe Accuracy Error Extraction Data Preprocessing CaptureWorkflow Start CaptureWorkflow End Data Preprocessing(Apply) ParameterOptimization PreliminaryFeature Selection SVM in C-val(All Attributes) SVM in C-Val(Selected Features) ROC Curve Naive Bayes inC-Val and Holdout Box Plot Box Plot Box Plot ROC Curve Table Space Connector Workflow Writer Table Writer Table Writer Table Writer Table Writer Space Connector Rule Engine Rule Engine Table Writer Column Renamer Column Filter

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Created by: AlfredLaptop
Created at: 2024-04-08
On NodePit since: 2024-06-11
Last update: 2024-09-16
Created with KNIME version: v5.2.3

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