Comparison of Linear Regression, Decision Tree Regression, and Random Forest Regression Algorithms in Predicting Baldness Risk

    Sebastianus Adi Santoso Mola, Alfonsus Maria De Liguori Goru, Christian Jaquelino Lamapaha, Yoseph Kurubingan Bekayo
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    Studysummary This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.
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