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08_​Guided_​Analytics

Choose & Read DataDropdown menus to choosedataset for : - training - testingfrom the datasets between2007 and 2008.Read training set and test set Choose Target Variable & Prediction Algorithms- Dropdown menu to choose the target variable- Table selection between algorithms. Possible options: - Decision Tree - Gradient Boosted Tree - Random Forest - Neural Network - Current Model Model Training- Train the chosen models - Join probabilities of P(delay) ROC Curve & Lift Chart- ROC Curve with all chosenalgorithms- 2 Lift Charts for the 2 bestperforming algorithms based onROC curve This workflow creates a model selction on the webportal, where data analysts can choose a training and a test set between the datasets of 2007 to 2008, the target variable and the algorithms, which should be considered for the modelcomparison. The results are then displayed in a ROC Curve. For further comparison the best two algorithms showed in the ROC curve are compared via a Lift Chart. Finally, the best performing algorithm is chosen to be used in production. Download the BestPerforming Model- Download the best modelin .table format Choose & Read Data ROC Curve & LiftChart Selection Download theBest Model Model Training Choose Algorithms Variable ConditionLoop End Generic Loop Start Table Row to Variable(deprecated) Choose TargetVariable Choose & Read DataDropdown menus to choosedataset for : - training - testingfrom the datasets between2007 and 2008.Read training set and test set Choose Target Variable & Prediction Algorithms- Dropdown menu to choose the target variable- Table selection between algorithms. Possible options: - Decision Tree - Gradient Boosted Tree - Random Forest - Neural Network - Current Model Model Training- Train the chosen models - Join probabilities of P(delay) ROC Curve & Lift Chart- ROC Curve with all chosenalgorithms- 2 Lift Charts for the 2 bestperforming algorithms based onROC curve This workflow creates a model selction on the webportal, where data analysts can choose a training and a test set between the datasets of 2007 to 2008, the target variable and the algorithms, which should be considered for the modelcomparison. The results are then displayed in a ROC Curve. For further comparison the best two algorithms showed in the ROC curve are compared via a Lift Chart. Finally, the best performing algorithm is chosen to be used in production. Download the BestPerforming Model- Download the best modelin .table format Choose & Read Data ROC Curve & LiftChart Selection Download theBest Model Model Training Choose Algorithms Variable ConditionLoop End Generic Loop Start Table Row to Variable(deprecated) Choose TargetVariable

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Created by: kathrinmelcher
Created at: 2017-07-19
On NodePit since: 2019-07-12
Last update: 2024-04-18
Created with KNIME version: v4.1.0

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