回归模型比较分析的可视化

This study presents a visualization method for comparative analysis of regression models, addressing the challenges associated with selecting appropriate methods for specific applications. Regression is a well-studied problem, and various methods have been proposed, each requiring different hyper-parameter settings. Performance comparison is crucial, typically measured using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or R-squared (R²). While these metrics provide numerical summaries of predictive accuracy by quantifying the difference between predicted and actual values, they often aggregate too much information, making it difficult to discern the performance differences of models in various contexts. The proposed visualization aims to enhance the interpretability of model performance, facilitating better decision-making in model selection.