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20230919 Pikairos JustKNIMEIt Season 2 Challenge 25 Detecting the Presence of Heart Disease

20230919 Pikairos JustKnimeIt Season 2 Challenge 25 Detecting the Presence of Heart Disease

You work as a data scientist for a healthcare company attempting to create a predictor for the presence of heart disease in patients. Currently, you are experimenting with 11 different features (potential heart disease indicators) and the XGBoost classification model, and you noticed that its performance can change quite a bit depending on how it is tuned. In this challenge, you will implement hyperparameter tuning to find the best values for XGBoost's Number of Boosting Rounds, Max Tree Depth, and learning rate hyperparameters. Use metric F-Measure as the objective function for tuning.

Challenge 25: Detecting the Presence of Heart DiseaseYou work as a data scientist for a healthcare company attempting to create a predictor for the presence of heart disease in patients. Currently, you areexperimenting with 11 different features (potential heart disease indicators) and the XGBoost classification model, and you noticed that its performancecan change quite a bit depending on how it is tuned. In this challenge, you will implement hyperparameter tuning to find the best values for XGBoost'sNumber of Boosting Rounds, Max Tree Depth, and learning rate hyperparameters. Use metric F-Measure as the objective function for tuning. Readheart.csvDataPredict HeartDisease Basedon XGBoostModelConvertHeart DiseaseColumn to a StringPerform 5-foldCross ValidationCollect BestParametersfrom Each CrossValidation LoopCollect BestParametersBrute ForceOptimizationTrain ModelSplit intoTraining and TestSetsSort in DescendingOrder byObjective ValueColumnCreate VariablesTrain ModelUsing the Parametersthat Gave the HighestObjective Function Outof All Cross ValidationsCalculateStatisticsPredict HeartDisease Basedon XGBoostModelCalculateStatisticsAggregateMinimumF-MeasureCreate F-MeasureVariableAggregateMinimumF-Measure File Reader XGBoost Predictor String Manipulation X-Partitioner Loop End ParameterOptimization Loop End Parameter OptimizationLoop Start XGBoost TreeEnsemble Learner Partitioning Sorter Table Rowto Variable XGBoost TreeEnsemble Learner Scorer XGBoost Predictor Scorer (JavaScript) GroupBy Table Rowto Variable GroupBy Challenge 25: Detecting the Presence of Heart DiseaseYou work as a data scientist for a healthcare company attempting to create a predictor for the presence of heart disease in patients. Currently, you areexperimenting with 11 different features (potential heart disease indicators) and the XGBoost classification model, and you noticed that its performancecan change quite a bit depending on how it is tuned. In this challenge, you will implement hyperparameter tuning to find the best values for XGBoost'sNumber of Boosting Rounds, Max Tree Depth, and learning rate hyperparameters. Use metric F-Measure as the objective function for tuning. Readheart.csvDataPredict HeartDisease Basedon XGBoostModelConvertHeart DiseaseColumn to a StringPerform 5-foldCross ValidationCollect BestParametersfrom Each CrossValidation LoopCollect BestParametersBrute ForceOptimizationTrain ModelSplit intoTraining and TestSetsSort in DescendingOrder byObjective ValueColumnCreate VariablesTrain ModelUsing the Parametersthat Gave the HighestObjective Function Outof All Cross ValidationsCalculateStatisticsPredict HeartDisease Basedon XGBoostModelCalculateStatisticsAggregateMinimumF-MeasureCreate F-MeasureVariableAggregateMinimumF-Measure File Reader XGBoost Predictor String Manipulation X-Partitioner Loop End ParameterOptimization Loop End Parameter OptimizationLoop Start XGBoost TreeEnsemble Learner Partitioning Sorter Table Rowto Variable XGBoost TreeEnsemble Learner Scorer XGBoost Predictor Scorer (JavaScript) GroupBy Table Rowto Variable GroupBy

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Created by: Molina
Created at: 2023-09-19
On NodePit since: 2023-09-20
Last update: 2024-04-27
Created with KNIME version: v4.7.4
Tags: JKISeason2-25X-PartitionerParameter Optimization Loop Start

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