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01_​Experiment_​logging

Training workflow: Experiment loggingThis workflow preprocesses the data, trains the credit scoring model, saves the deployment workflow on KNIME Business Hub, and writes the reference predictions to the database. Besides, the workflow logs all the trainingartifacts, such as timestamp, training data metadata, deployment workflow summary, model hyperparameters, deployment workflow location, and model performance to the csv file. Extract thetimestamp Extract the metadata about the training and test data Capture modelsconfiguration:algorithm, hyper-parameters, features,target, etc. Save the wholeprediction workflowwith preprocessing,model(s), andpredictor Save referencepredictions formonitoring Logging workflow metadata, model performance, models hyper-parameters, model feature importance, etc. Find the backup datain ../data folderExecutiondate & timeModelsummaryCreate onerowAdd projectnameWorkflow path,name, timestampAppend toModelsLogFile.csv75% training 25% testStandardize thelog table structureFeature Importance CaptureWorkflow End Date&TimeConfiguration Workflow SummaryExtractor Column Appender ConstantValue Column Variable toTable Row Binary ClassificationInspector CSV Writer Path to String(Variable) Data Preprocessing Data Preprocessing(Apply) CaptureWorkflow Start Partitioning Workflow Writer Table Manipulator Save ReferencePredictions ContainerInput (Table) Training data PostgreSQLConnector XGBoost Predictor Table to JSON XGBoost TreeEnsemble Learner Training workflow: Experiment loggingThis workflow preprocesses the data, trains the credit scoring model, saves the deployment workflow on KNIME Business Hub, and writes the reference predictions to the database. Besides, the workflow logs all the trainingartifacts, such as timestamp, training data metadata, deployment workflow summary, model hyperparameters, deployment workflow location, and model performance to the csv file. Extract thetimestamp Extract the metadata about the training and test data Capture modelsconfiguration:algorithm, hyper-parameters, features,target, etc. Save the wholeprediction workflowwith preprocessing,model(s), andpredictor Save referencepredictions formonitoring Logging workflow metadata, model performance, models hyper-parameters, model feature importance, etc. Find the backup datain ../data folderExecutiondate & timeModelsummaryCreate onerowAdd projectnameWorkflow path,name, timestampAppend toModelsLogFile.csv75% training 25% testStandardize thelog table structureFeature Importance CaptureWorkflow End Date&TimeConfiguration Workflow SummaryExtractor Column Appender ConstantValue Column Variable toTable Row Binary ClassificationInspector CSV Writer Path to String(Variable) Data Preprocessing Data Preprocessing(Apply) CaptureWorkflow Start Partitioning Workflow Writer Table Manipulator Save ReferencePredictions ContainerInput (Table) Training data PostgreSQLConnector XGBoost Predictor Table to JSON XGBoost TreeEnsemble Learner

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Lada.Rudnitckaia
Created at: 2022-10-07
On NodePit since: 2023-07-15
Last update: 2024-04-26
Created with KNIME version: v4.7.5

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