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JKISeasor2-25_​tomljh_​ver2

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Challenge 25: Detecting the Presence of Heart Disease

Challenge 25: Detecting the Presence of Heart DiseaseLevel: MediumDescription: You work as a data scientist for a healthcare company attempting to create a predictor for the presence ofheart disease in patients. Currently, you are experimenting with 11 different features (potential heart diseaseindicators) and the XGBoost classification model, and you noticed that its performance can change quite a bitdepending on how it is tuned. In this challenge, you will implement hyperparameter tuning to find the best values forXGBoost's Number of Boosting Rounds, Max Tree Depth, and learning rate hyperparameters. Use metric F-Measureas the objective function for tuning.Author: Keerthan ShettyData Description： https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction step 1: Optimize hyperparameters Step 2: Based on the optimal hyperparameter reproduction results Readheart.csvtarget columnint - > stringtrain:70%test:30%search strategie ：TPEMax. number of iterations：200Using 'Macro F1' as the optimization objectiveNote: The Row ID is the target column valueMacro F1Note: Look Interactive View CSV Reader Number To String Partitioning Parameter OptimizationLoop Start ParameterOptimization Loop End Row Filter Table RowTo Variable Box Plot Table RowTo Variable XGBoost Model andCal Macro F1 XGBoost Model andCal Macro F1 Challenge 25: Detecting the Presence of Heart DiseaseLevel: MediumDescription: You work as a data scientist for a healthcare company attempting to create a predictor for the presence ofheart disease in patients. Currently, you are experimenting with 11 different features (potential heart diseaseindicators) and the XGBoost classification model, and you noticed that its performance can change quite a bitdepending on how it is tuned. In this challenge, you will implement hyperparameter tuning to find the best values forXGBoost's Number of Boosting Rounds, Max Tree Depth, and learning rate hyperparameters. Use metric F-Measureas the objective function for tuning.Author: Keerthan ShettyData Description：https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction step 1: Optimize hyperparameters Step 2: Based on the optimal hyperparameter reproduction results Readheart.csvtarget columnint - > stringtrain:70%test:30%search strategie ：TPEMax. number of iterations：200Using 'Macro F1' as the optimization objectiveNote: The Row ID is the target column valueMacro F1Note: Look Interactive ViewCSV Reader Number To String Partitioning Parameter OptimizationLoop Start ParameterOptimization Loop End Row Filter Table RowTo Variable Box Plot Table RowTo Variable XGBoost Model andCal Macro F1 XGBoost Model andCal Macro F1

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Created by: Admin
Created at: 2023-09-13
On NodePit since: 2023-09-14
Last update: 2024-04-27
Created with KNIME version: v5.1.0
Tags: JKISeason2-25

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