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JKISeason2-25_​sryu

Challenge 25: Detecting the Presence of Heart Disease
Level: Medium

Description: 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.

Hyperparameter Optimization Prediction of test setbest parametersF-measure_meanOptimization by TPERe-training using all training data with optimized parameterstrain / test70 / 30Stratified samplimgdata5CVStratified samplimgrandom seed = 0F-measure_mean XGBoost Predictor Table Rowto Variable ParameterOptimization Loop End Parameter OptimizationLoop Start XGBoost TreeEnsemble Learner Scorer XGBoost Predictor XGBoost TreeEnsemble Learner Partitioning Table Rowto Variable CSV Reader Scorer X-Aggregator X-Partitioner Math Formula Hyperparameter Optimization Prediction of test setbest parametersF-measure_meanOptimization by TPERe-training using all training data with optimized parameterstrain / test70 / 30Stratified samplimgdata5CVStratified samplimgrandom seed = 0F-measure_meanXGBoost Predictor Table Rowto Variable ParameterOptimization Loop End Parameter OptimizationLoop Start XGBoost TreeEnsemble Learner Scorer XGBoost Predictor XGBoost TreeEnsemble Learner Partitioning Table Rowto Variable CSV Reader Scorer X-Aggregator X-Partitioner Math Formula

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Created by: sryus
Created at: 2023-09-14
On NodePit since: 2023-09-15
Last update: 2024-04-27
Created with KNIME version: v4.7.4
Tags: JKISeason2-25

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