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

This study compared the effectiveness of Linear Regression, Decision Tree Regression, and Random Forest Regression in predicting baldness risk using a dataset of 5925 samples with variables like age, gender, and lifestyle factors. Random Forest Regression was found to be the most effective, achieving the lowest Mean Squared Error (0.0979) and highest R² (0.9056), outperforming the other models. Hyperparameter optimization via Grid Search improved model performance. The study concludes that Random Forest Regression is best suited for complex datasets, while Linear Regression is a viable option for simpler datasets.