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03_​Bioactivity_​Prediction_​Learn_​Testing

03_Bioactivity_Prediction_Learn_Testing

This workflow is used for learning the model. It deploys a simple version of parameter optimzation protocol designed for testing the Model Factory system. In this version two machine learning methods are used and parameter optimization is performed twice. For the full version see workflow 03_Bioactivity_Prediction_Learn_All_Methods.

03_Bioactivity_Prediction_Learn_Testing This workflow is used for learning the model. It deploys a simple version of parameter optimzation protocol designed for testing the Model Factory system. In this version two machine learning methods are used and parameteroptimization is performed twice. For the full version see workflow 03_Bioactivity_Prediction_Learn_All_Methods. Input: It will get the input file as prepared by Transform workflow andOutput: The learned model with its statistics in //Metainfo/Bioactivity/model_output_ASSAY_ID_timestamp.table and the statistics for the best model per each method and iteration in //Metainfo/Bioactivity/best_models_stats_ASSAY_ID_timestamp.table. Framework (Connection to Model Factory) Output to Model Factory Custom Workflow for Model Building Step Node 8Node 9RESULTSelected model2 timesload_output_assayid.table80/20random stratifiedNode 176Collect Stats of best modelpunctuation in timeseedwith iterationsADDITIONALall_models_stats JSON to Table Table Rowto Variable Table Writer Counting Loop Start Table Reader Partitioning Naive Bayes Random Forest String Manipulation(Variable) Create Date&TimeRange Table Rowto Variable ContainerInput (JSON) Loop End String Replacer Math Formula(Variable) Concatenate(Optional in) String Manipulation(Variable) Table Writer Sort and Group Build the Bestand Score Select the mostCommon model 03_Bioactivity_Prediction_Learn_Testing This workflow is used for learning the model. It deploys a simple version of parameter optimzation protocol designed for testing the Model Factory system. In this version two machine learning methods are used and parameteroptimization is performed twice. For the full version see workflow 03_Bioactivity_Prediction_Learn_All_Methods. Input: It will get the input file as prepared by Transform workflow andOutput: The learned model with its statistics in //Metainfo/Bioactivity/model_output_ASSAY_ID_timestamp.table and the statistics for the best model per each method and iteration in //Metainfo/Bioactivity/best_models_stats_ASSAY_ID_timestamp.table. Framework (Connection to Model Factory) Output to Model Factory Custom Workflow for Model Building Step Node 8Node 9RESULTSelected model2 timesload_output_assayid.table80/20random stratifiedNode 176Collect Stats of best modelpunctuation in timeseedwith iterationsADDITIONALall_models_stats JSON to Table Table Rowto Variable Table Writer Counting Loop Start Table Reader Partitioning Naive Bayes Random Forest String Manipulation(Variable) Create Date&TimeRange Table Rowto Variable ContainerInput (JSON) Loop End String Replacer Math Formula(Variable) Concatenate(Optional in) String Manipulation(Variable) Table Writer Sort and Group Build the Bestand Score Select the mostCommon model

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