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Detecting the Presence of Heart Disease

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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.

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csv heart deseaselearnerhyperparameters7525predictf-measuremeanCSV Reader Number To String XGBoost TreeEnsemble Learner Parameter OptimizationLoop Start ParameterOptimization Loop End Partitioning XGBoost Predictor Scorer GroupBy Table RowTo Variable csv heart deseaselearnerhyperparameters7525predictf-measuremeanCSV Reader Number To String XGBoost TreeEnsemble Learner Parameter OptimizationLoop Start ParameterOptimization Loop End Partitioning XGBoost Predictor Scorer GroupBy Table RowTo Variable

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Created by: th
Created at: 2023-09-18
On NodePit since: 2023-09-19
Last update: 2024-04-27
Created with KNIME version: v5.1.0
Tags: JKISeason2-25JKISeason2

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